This work is licensed under the Creative Commons Attribution 4.0 International License.
Cheok, Ming Jin, Zaid Omar, and Mohamed Hisham Jaward. “A review of hand gesture and sign language recognition techniques.” International Journal of Machine Learning and Cybernetics 10 (2019): 131–153.CheokMing JinOmarZaidJawardMohamed Hisham“A review of hand gesture and sign language recognition techniques.”International Journal of Machine Learning and Cybernetics102019131153Search in Google Scholar
World Federation of the deaf. Rome, Italy. Retrieved from http://wfdeaf.org/our-work/. (Accessed 18 January 2023).World Federation of the deafRome, ItalyRetrieved from http://wfdeaf.org/our-work/. (Accessed 18 January 2023).Search in Google Scholar
Abd Al-Latief, Shahad Thamear, Salman Yussof, Azhana Ahmad, Saif Mohanad Khadim, and Raed Abdulkareem Abdulhasan. “Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning.” International Journal of Networked and Distributed Computing (2024): 1–18.Al-LatiefAbdThamearShahadYussofSalmanAhmadAzhanaKhadimSaif MohanadAbdulhasanRaed Abdulkareem“Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning.”International Journal of Networked and Distributed Computing2024118Search in Google Scholar
Druzhkov, P. N., and V. D. Kustikova. “A survey of deep learning methods and software tools for image classification and object detection.” Pattern Recognition and Image Analysis 26 (2016): 9–15.DruzhkovP. N.KustikovaV. D.“A survey of deep learning methods and software tools for image classification and object detection.”Pattern Recognition and Image Analysis262016915Search in Google Scholar
Wu, Di, Nabin Sharma, and Michael Blumenstein. “Recent advances in video-based human action recognition using deep learning: A review.” In 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2865–2872. IEEE, 2017.WuDiSharmaNabinBlumensteinMichael“Recent advances in video-based human action recognition using deep learning: A review.”In2017 International Joint Conference on Neural Networks (IJCNN)28652872IEEE2017Search in Google Scholar
Hussain, Soeb, Rupal Saxena, Xie Han, Jameel Ahmed Khan, and Hyunchul Shin. “Hand gesture recognition using deep learning.” In 2017 International SoC design conference (ISOCC), pp. 48–49. IEEE, 2017.HussainSoebSaxenaRupalHanXieKhanJameel AhmedShinHyunchul“Hand gesture recognition using deep learning.”In2017 International SoC design conference (ISOCC)4849IEEE2017Search in Google Scholar
Alexiadis, Dimitrios S., Anargyros Chatzitofis, Nikolaos Zioulis, Olga Zoidi, Georgios Louizis, Dimitrios Zarpalas, and Petros Daras. “An integrated platform for live 3D human reconstruction and motion capturing.” IEEE Transactions on Circuits and Systems for Video Technology 27, no. 4 (2016): 798–813.AlexiadisDimitrios S.ChatzitofisAnargyrosZioulisNikolaosZoidiOlgaLouizisGeorgiosZarpalasDimitriosDarasPetros“An integrated platform for live 3D human reconstruction and motion capturing.”IEEE Transactions on Circuits and Systems for Video Technology2742016798813Search in Google Scholar
Adaloglou, Nikolas, Theocharis Chatzis, Ilias Papastratis, Andreas Stergioulas, Georgios Th Papadopoulos, Vassia Zacharopoulou, George J. Xydopoulos, Klimnis Atzakas, Dimitris Papazachariou, and Petros Daras. “A comprehensive study on deep learning-based methods for sign language recognition.” IEEE Transactions on Multimedia 24 (2021): 1750–1762.AdaloglouNikolasChatzisTheocharisPapastratisIliasStergioulasAndreasPapadopoulosGeorgios ThZacharopoulouVassiaXydopoulosGeorge J.AtzakasKlimnisPapazachariouDimitrisDarasPetros“A comprehensive study on deep learning-based methods for sign language recognition.”IEEE Transactions on Multimedia24202117501762Search in Google Scholar
Subburaj, S., and S. Murugavalli. “Survey on sign language recognition in context of vision-based and deep learning.” Measurement: Sensors 23 (2022): 100385.SubburajS.MurugavalliS.“Survey on sign language recognition in context of vision-based and deep learning.”Measurement: Sensors232022100385Search in Google Scholar
Mandel, M. (1977). Iconic devices in American sign language. On the other hand, New perspectives on American sign language.MandelM.1977Iconic devices in American sign language. On the other hand, New perspectives on American sign languageSearch in Google Scholar
Sandler, Wendy, and Diane Lillo-Martin. Sign language and linguistic universals. Cambridge University Press, 2006.SandlerWendyLillo-MartinDianeSign language and linguistic universalsCambridge University Press2006Search in Google Scholar
Goldin-Meadow, Susan, and Diane Brentari. “Gesture, sign, and language: The coming of age of sign language and gesture studies.” Behavioral and brain sciences 40 (2017): e46.Goldin-MeadowSusanBrentariDiane“Gesture, sign, and language: The coming of age of sign language and gesture studies.”Behavioral and brain sciences402017e46Search in Google Scholar
Ong, Sylvie CW, and Surendra Ranganath. “Automatic sign language analysis: A survey and the future beyond lexical meaning.” IEEE Transactions on Pattern Analysis & Machine Intelligence 27, no. 06 (2005): 873–891.OngSylvie CWRanganathSurendra“Automatic sign language analysis: A survey and the future beyond lexical meaning.”IEEE Transactions on Pattern Analysis & Machine Intelligence27062005873891Search in Google Scholar
Joudaki, Saba, Dzulkifli bin Mohamad, Tanzila Saba, Amjad Rehman, Mznah Al-Rodhaan, and Abdullah Al-Dhelaan. “Vision-based sign language classification: a directional review.” IETE Technical Review 31, no. 5 (2014): 383–391.JoudakiSababin MohamadDzulkifliSabaTanzilaRehmanAmjadAl-RodhaanMznahAl-DhelaanAbdullah“Vision-based sign language classification: a directional review.”IETE Technical Review3152014383391Search in Google Scholar
Sharma, Sakshi, and Sukhwinder Singh. “Vision-based sign language recognition system: A Comprehensive Review.” In 2020 international conference on inventive computation technologies (ICICT), pp. 140–144. IEEE, 2020.SharmaSakshiSinghSukhwinder“Vision-based sign language recognition system: A Comprehensive Review.”In2020 international conference on inventive computation technologies (ICICT)140144IEEE2020Search in Google Scholar
Pansare, Jayshree R., and Maya Ingle. “Vision-based approach for American sign language recognition using edge orientation histogram.” In 2016 international conference on image, vision and computing (ICIVC), pp. 86–90. IEEE, 2016.PansareJayshree R.IngleMaya“Vision-based approach for American sign language recognition using edge orientation histogram.”In2016 international conference on image, vision and computing (ICIVC)8690IEEE2016Search in Google Scholar
Aran, Oya. “Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components.” Bogazi» ci University (2008).AranOya“Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components.”Bogazi» ci University2008Search in Google Scholar
Al-Qurishi, Muhammad, Thariq Khalid, and Riad Souissi. “Deep learning for sign language recognition: Current techniques, benchmarks, and open issues.” IEEE Access 9 (2021): 126917–126951.Al-QurishiMuhammadKhalidThariqSouissiRiad“Deep learning for sign language recognition: Current techniques, benchmarks, and open issues.”IEEE Access92021126917126951Search in Google Scholar
Li, Kehuang, Zhengyu Zhou, and Chin-Hui Lee. “Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applications.” ACM Transactions on Accessible Computing (TACCESS) 8, no. 2 (2016): 1–23.LiKehuangZhouZhengyuLeeChin-Hui“Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applications.”ACM Transactions on Accessible Computing (TACCESS)822016123Search in Google Scholar
Rosero-Montalvo, Paul D., Pamela Godoy-Trujillo, Edison Flores-Bosmediano, Jorge Carrascal-Garcia, Santiago Otero-Potosi, Henry Benitez-Pereira, and Diego H. Peluffo-Ordonez. “Sign language recognition based on intelligent glove using machine learning techniques.” In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), pp. 1–5. IEEE, 2018.Rosero-MontalvoPaul D.Godoy-TrujilloPamelaFlores-BosmedianoEdisonCarrascal-GarciaJorgeOtero-PotosiSantiagoBenitez-PereiraHenryPeluffo-OrdonezDiego H.“Sign language recognition based on intelligent glove using machine learning techniques.”In2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)15IEEE2018Search in Google Scholar
Kudrinko, Karly, Emile Flavin, Xiaodan Zhu, and Qingguo Li. “Wearable sensor-based sign language recognition: A comprehensive review.” IEEE Reviews in Biomedical Engineering 14 (2020): 82–97.KudrinkoKarlyFlavinEmileZhuXiaodanLiQingguo“Wearable sensor-based sign language recognition: A comprehensive review.”IEEE Reviews in Biomedical Engineering1420208297Search in Google Scholar
Li, Shao-Zi, Bin Yu, Wei Wu, Song-Zhi Su, and Rong-Rong Ji. “Feature learning based on SAE–PCA network for human gesture recognition in RGBD images.” Neurocomputing 151 (2015): 565–573.LiShao-ZiYuBinWuWeiSuSong-ZhiJiRong-Rong“Feature learning based on SAE–PCA network for human gesture recognition in RGBD images.”Neurocomputing1512015565573Search in Google Scholar
Amin, Muhammad Saad, Syed Tahir Hussain Rizvi, and Md Murad Hossain. “A Comparative Review on Applications of Different Sensors for Sign Language Recognition.” Journal of Imaging 8, no. 4 (2022): 98.AminMuhammad SaadRizviSyed Tahir HussainHossainMd Murad“A Comparative Review on Applications of Different Sensors for Sign Language Recognition.”Journal of Imaging84202298Search in Google Scholar
Theodorakis, Stavros, Vassilis Pitsikalis, and Petros Maragos. “Dynamic–static unsupervised sequentiality, statistical subunits and lexicon for sign language recognition.” Image and Vision Computing 32, no. 8 (2014): 533–549.TheodorakisStavrosPitsikalisVassilisMaragosPetros“Dynamic–static unsupervised sequentiality, statistical subunits and lexicon for sign language recognition.”Image and Vision Computing3282014533549Search in Google Scholar
Plouffe, Guillaume, and Ana-Maria Cretu. “Static and dynamic hand gesture recognition in depth data using dynamic time warping.” IEEE transactions on instrumentation and measurement 65, no. 2 (2015): 305–316.PlouffeGuillaumeCretuAna-Maria“Static and dynamic hand gesture recognition in depth data using dynamic time warping.”IEEE transactions on instrumentation and measurement6522015305316Search in Google Scholar
Agrawal, Subhash Chand, Anand Singh Jalal, and Rajesh Kumar Tripathi. “A survey on manual and non-manual sign language recognition for isolated and continuous sign.” International Journal of Applied Pattern Recognition 3, no. 2 (2016): 99–134.AgrawalSubhash ChandJalalAnand SinghTripathiRajesh Kumar“A survey on manual and non-manual sign language recognition for isolated and continuous sign.”International Journal of Applied Pattern Recognition32201699134Search in Google Scholar
El-Alfy, El-Sayed M., and Hamzah Luqman. “A comprehensive survey and taxonomy of sign language research.” Engineering Applications of Artificial Intelligence 114 (2022): 105198.El-AlfyEl-Sayed M.LuqmanHamzah“A comprehensive survey and taxonomy of sign language research.”Engineering Applications of Artificial Intelligence1142022105198Search in Google Scholar
Dong, Shi, Ping Wang, and Khushnood Abbas. “A survey on deep learning and its applications.” Computer Science Review 40 (2021): 100379.DongShiWangPingAbbasKhushnood“A survey on deep learning and its applications.”Computer Science Review402021100379Search in Google Scholar
Najafabadi, Maryam M., Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic. “Deep learning applications and challenges in big data analytics.” Journal of big data 2, no. 1 (2015): 1–21.NajafabadiMaryam M.VillanustreFlavioKhoshgoftaarTaghi M.SeliyaNaeemWaldRandallMuharemagicEdin“Deep learning applications and challenges in big data analytics.”Journal of big data212015121Search in Google Scholar
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521, no. 7553 (2015): 436–444.LeCunYannBengioYoshuaHintonGeoffrey“Deep learning.”nature52175532015436444Search in Google Scholar
Sarker, Iqbal H. “Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions.” SN Computer Science 2, no. 6 (2021): 420.SarkerIqbal H.“Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions.”SN Computer Science262021420Search in Google Scholar
Rastgoo, Razieh, Kourosh Kiani, Sergio Escalera, and Mohammad Sabokrou. “Sign language production: A review.” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3451–3461. 2021.RastgooRaziehKianiKouroshEscaleraSergioSabokrouMohammad“Sign language production: A review.”InProceedings of the IEEE/CVF conference on computer vision and pattern recognition345134612021Search in Google Scholar
Yadav, Ashima, and Dinesh Kumar Vishwakarma. “Sentiment analysis using deep learning architectures: a review.” Artificial Intelligence Review 53, no. 6 (2020): 4335–4385.YadavAshimaVishwakarmaDinesh Kumar“Sentiment analysis using deep learning architectures: a review.”Artificial Intelligence Review536202043354385Search in Google Scholar
Abdulhasan, Raed Abdulkareem, Shahad Thamear Abd Al-latief, and Saif Mohanad Kadhim. “Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system.” Multimedia Tools and Applications 83, no. 11 (2024): 32099–32122.AbdulhasanRaed AbdulkareemAbd Al-latiefShahad ThamearKadhimSaif Mohanad“Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system.”Multimedia Tools and Applications831120243209932122Search in Google Scholar
Madhiarasan, Dr M., Prof Roy, and Partha Pratim. “A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets.” arXiv preprint arXiv:2204.03328 (2022).MadhiarasanDr M.Prof RoyPartha Pratim“A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets.”arXiv preprint arXiv:2204.033282022Search in Google Scholar
Yang, Hee-Deok, and Seong-Whan Lee. “Robust sign language recognition by combining manual and non-manual features based on conditional random field and support vector machine.” Pattern Recognition Letters 34, no. 16 (2013): 2051–2056.YangHee-DeokLeeSeong-Whan“Robust sign language recognition by combining manual and non-manual features based on conditional random field and support vector machine.”Pattern Recognition Letters3416201320512056Search in Google Scholar
Chen, Feng-Sheng, Chih-Ming Fu, and Chung-Lin Huang. “Hand gesture recognition using a real-time tracking method and hidden Markov models.” Image and vision computing 21, no. 8 (2003): 745–758.ChenFeng-ShengFuChih-MingHuangChung-Lin“Hand gesture recognition using a real-time tracking method and hidden Markov models.”Image and vision computing2182003745758Search in Google Scholar
Ibrahim, Nada B., Hala H. Zayed, and Mazen M. Selim. “Advances, challenges and opportunities in continuous sign language recognition.” J. Eng. Appl. Sci 15, no. 5 (2020): 1205–1227.IbrahimNada B.ZayedHala H.SelimMazen M.“Advances, challenges and opportunities in continuous sign language recognition.”J. Eng. Appl. Sci155202012051227Search in Google Scholar
Smith, Paul, Niels da Vitoria Lobo, and Mubarak Shah. “Resolving hand over face occlusion.” Image and Vision Computing 25, no. 9 (2007): 1432–1448.SmithPaulLoboNiels da VitoriaShahMubarak“Resolving hand over face occlusion.”Image and Vision Computing259200714321448Search in Google Scholar
Yang, Ruiduo, Sudeep Sarkar, and Barbara Loeding. “Handling movement epenthesis and hand segmentation ambiguities in continuous sign language recognition using nested dynamic programming.” IEEE transactions on pattern analysis and machine intelligence 32, no. 3 (2009): 462–477.YangRuiduoSarkarSudeepLoedingBarbara“Handling movement epenthesis and hand segmentation ambiguities in continuous sign language recognition using nested dynamic programming.”IEEE transactions on pattern analysis and machine intelligence3232009462477Search in Google Scholar
Zhang, Hui, Jason E. Fritts, and Sally A. Goldman. “Image segmentation evaluation: A survey of unsupervised methods.” computer vision and image understanding 110, no. 2 (2008): 260–280.ZhangHuiFrittsJason E.GoldmanSally A.“Image segmentation evaluation: A survey of unsupervised methods.”computer vision and image understanding11022008260280Search in Google Scholar
Cai, Shanshan, and Desheng Liu. “A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images.” Remote sensing letters 4, no. 10 (2013): 998–1007.CaiShanshanLiuDesheng“A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images.”Remote sensing letters41020139981007Search in Google Scholar
Kausar, Sumaira, and M. Younus Javed. “A survey on sign language recognition.” In 2011 Frontiers of Information Technology, pp. 95–98. IEEE, 2011.KausarSumairaJavedM. Younus“A survey on sign language recognition.”In2011 Frontiers of Information Technology9598IEEE2011Search in Google Scholar
Aloysius, Neena, and M. Geetha. “Understanding vision-based continuous sign language recognition.” Multimedia Tools and Applications 79, no. 31–32 (2020): 22177–22209.AloysiusNeenaGeethaM.“Understanding vision-based continuous sign language recognition.”Multimedia Tools and Applications7931–3220202217722209Search in Google Scholar
https://www.kaggle.com/grassknoted/asl-alphabethttps://www.kaggle.com/grassknoted/asl-alphabetSearch in Google Scholar
https://www.kaggle.com/datasets/datamunge/sign-language-mnisthttps://www.kaggle.com/datasets/datamunge/sign-language-mnistSearch in Google Scholar
Pugeault, Nicolas, and Richard Bowden. “Spelling it out: Real-time ASL fingerspelling recognition.” In 2011 IEEE International conference on computer vision workshops (ICCV workshops), pp. 1114–1119. IEEE, 2011.PugeaultNicolasBowdenRichard“Spelling it out: Real-time ASL fingerspelling recognition.”In2011 IEEE International conference on computer vision workshops (ICCV workshops)11141119IEEE2011Search in Google Scholar
Tompson, Jonathan, Murphy Stein, Yann Lecun, and Ken Perlin. “Real-time continuous pose recovery of human hands using convolutional networks.” ACM Transactions on Graphics (ToG) 33, no. 5 (2014): 1–10.TompsonJonathanSteinMurphyLecunYannPerlinKen“Real-time continuous pose recovery of human hands using convolutional networks.”ACM Transactions on Graphics (ToG)3352014110Search in Google Scholar
Ong, Eng-Jon, Helen Cooper, Nicolas Pugeault, and Richard Bowden. “Sign language recognition using sequential pattern trees.” In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2200–2207. IEEE, 2012.OngEng-JonCooperHelenPugeaultNicolasBowdenRichard“Sign language recognition using sequential pattern trees.”In2012 IEEE Conference on Computer Vision and Pattern Recognition22002207IEEE2012Search in Google Scholar
Triesch, Jochen, and Christoph Von Der Malsburg. “Robust classification of hand postures against complex backgrounds.” In Proceedings of the second international conference on automatic face and gesture recognition, pp. 170–175. IEEE, 1996.TrieschJochenVon Der MalsburgChristoph“Robust classification of hand postures against complex backgrounds.”InProceedings of the second international conference on automatic face and gesture recognition170175IEEE1996Search in Google Scholar
Marin, Giulio, Fabio Dominio, and Pietro Zanuttigh. “Hand gesture recognition with leap motion and kinect devices.” In 2014 IEEE International conference on image processing (ICIP), pp. 1565–1569. IEEE, 2014.MarinGiulioDominioFabioZanuttighPietro“Hand gesture recognition with leap motion and kinect devices.”In2014 IEEE International conference on image processing (ICIP)15651569IEEE2014Search in Google Scholar
Ren, Zhou, Junsong Yuan, and Zhengyou Zhang. “Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera.” In Proceedings of the 19th ACM international conference on Multimedia, pp. 1093–1096. 2011.RenZhouYuanJunsongZhangZhengyou“Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera.”InProceedings of the 19th ACM international conference on Multimedia109310962011Search in Google Scholar
Feng, Bin, Fangzi He, Xinggang Wang, Yongjiang Wu, Hao Wang, Sihua Yi, and Wenyu Liu. “Depth-projection-map-based bag of contour fragments for robust hand gesture recognition.” IEEE Transactions on Human-Machine Systems 47, no. 4 (2016): 511–523.FengBinHeFangziWangXinggangWuYongjiangWangHaoYiSihuaLiuWenyu“Depth-projection-map-based bag of contour fragments for robust hand gesture recognition.”IEEE Transactions on Human-Machine Systems4742016511523Search in Google Scholar
Wilbur, Ronnie, and Avinash C. Kak. “Purdue RVL-SLLL American sign language database.” (2006).WilburRonnieKakAvinash C.“Purdue RVL-SLLL American sign language database.”2006Search in Google Scholar
Shi, Bowen, Aurora Martinez Del Rio, Jonathan Keane, Jonathan Michaux, Diane Brentari, Greg Shakhnarovich, and Karen Livescu. “American sign language fingerspelling recognition in the wild.” In 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 145–152. IEEE, 2018.ShiBowenDel RioAurora MartinezKeaneJonathanMichauxJonathanBrentariDianeShakhnarovichGregLivescuKaren“American sign language fingerspelling recognition in the wild.”In2018 IEEE Spoken Language Technology Workshop (SLT)145152IEEE2018Search in Google Scholar
Othman, Achraf, Zouhour Tmar, and Mohamed Jemni. “Toward developing a very big sign language parallel corpus.” In Computers Helping People with Special Needs: 13th International Conference, ICCHP 2012, Linz, Austria, July 11–13, 2012, Proceedings, Part II 13, pp. 192–199. Springer Berlin Heidelberg, 2012.OthmanAchrafTmarZouhourJemniMohamed“Toward developing a very big sign language parallel corpus.”InComputers Helping People with Special Needs: 13th International Conference, ICCHP 2012Linz, AustriaJuly 11–13, 2012Proceedings, Part II 13192199SpringerBerlin Heidelberg2012Search in Google Scholar
Neidle, Carol, and Augustine Opoku. A User’s Guide to the American Sign Language Linguistic Research Project (ASLLRP) Data Access Interface (DAI) 2—Version 2. American Sign Language Linguistic Research Project Report No. 18, Boston University. No. 18. Linguistic Research Project Report, 2020.NeidleCarolOpokuAugustineA User’s Guide to the American Sign Language Linguistic Research Project (ASLLRP) Data Access Interface (DAI) 2—Version 2American Sign Language Linguistic Research Project Report No. 18, Boston University. No. 18. Linguistic Research Project Report, 2020.Search in Google Scholar
Barczak, A. L. C., N. H. Reyes, M. Abastillas, A. Piccio, and Teo Susnjak. “A new 2D static hand gesture colour image dataset for ASL gestures.” (2011).BarczakA. L. C.ReyesN. H.AbastillasM.PiccioA.SusnjakTeo“A new 2D static hand gesture colour image dataset for ASL gestures.”2011Search in Google Scholar
http://vlm1.uta.edu/~srujana/ASLID/ASL_Image_Dataset.htmlhttp://vlm1.uta.edu/~srujana/ASLID/ASL_Image_Dataset.htmlSearch in Google Scholar
https://ieee-dataport.org/documents/ksu-arsl-arabic-sign-languagehttps://ieee-dataport.org/documents/ksu-arsl-arabic-sign-languageSearch in Google Scholar
Sidig, Ala Addin I., Hamzah Luqman, Sabri Mahmoud, and Mohamed Mohandes. “KArSL: Arabic sign language database.” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 20, no. 1 (2021): 1–19.SidigAla Addin I.LuqmanHamzahMahmoudSabriMohandesMohamed“KArSL: Arabic sign language database.”ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)2012021119Search in Google Scholar
Shanableh, Tamer, Khaled Assaleh, and Mohammad Al-Rousan. “Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37, no. 3 (2007): 641–650.ShanablehTamerAssalehKhaledAl-RousanMohammad“Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language.”IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)3732007641650Search in Google Scholar
https://www.idiap.ch/webarchives/sites/www.idiap.ch/resource/gestures/https://www.idiap.ch/webarchives/sites/www.idiap.ch/resource/gestures/Search in Google Scholar
https://github.com/DeepKothadiya/Custom_ISLDataset/tree/mainhttps://github.com/DeepKothadiya/Custom_ISLDataset/tree/mainSearch in Google Scholar
Forster, Jens, Christoph Schmidt, Oscar Koller, Martin Bellgardt, and Hermann Ney. “Extensions of the Sign Language Recognition and Translation Corpus RWTH-PHOENIX-Weather.” In LREC, pp. 1911–1916. 2014.ForsterJensSchmidtChristophKollerOscarBellgardtMartinNeyHermann“Extensions of the Sign Language Recognition and Translation Corpus RWTH-PHOENIX-Weather.”InLREC191119162014Search in Google Scholar
Agris, Ulrich von, and Karl-Friedrich Kraiss. “Signum database: Video corpus for signer-independent continuous sign language recognition.” In sign-lang@ LREC 2010, pp. 243–246. European Language Resources Association (ELRA), 2010.AgrisUlrich vonKraissKarl-Friedrich“Signum database: Video corpus for signer-independent continuous sign language recognition.”Insign-lang@ LREC 2010243246European Language Resources Association (ELRA)2010Search in Google Scholar
Chai, Xiujuan, Guang Li, Yushun Lin, Zhihao Xu, Yili Tang, Xilin Chen, and Ming Zhou. “Sign language recognition and translation with kinect.” In IEEE conf. on AFGR, vol. 655, p. 4. 2013.ChaiXiujuanLiGuangLinYushunXuZhihaoTangYiliChenXilinZhouMing“Sign language recognition and translation with kinect.”In IEEE conf. on AFGR65542013Search in Google Scholar
https://paperswithcode.com/dataset/csl-dailyhttps://paperswithcode.com/dataset/csl-dailySearch in Google Scholar
http://home.ustc.edu.cn/~pjh/openresources/cslr-dataset-2015/index.htmlhttp://home.ustc.edu.cn/~pjh/openresources/cslr-dataset-2015/index.htmlSearch in Google Scholar
Rafi, A.M.; Nawal, N.; Bayev, N.S.; Nima, L.; Shahnaz, C.; Fattah, S.A. Image-based bengali sign language alphabet recognition for deaf and dumb community. In Proceedings of the 2019 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 17–20 October 2019; pp. 1–7RafiA.M.NawalN.BayevN.S.NimaL.ShahnazC.FattahS.A.Image-based bengali sign language alphabet recognition for deaf and dumb communityInProceedings of the 2019 IEEE Global Humanitarian Technology Conference (GHTC)Seattle, WA, USA17–20 October 201917Search in Google Scholar
Islam, Md Sanzidul, Sadia Sultana Sharmin Mousumi, Nazmul A. Jessan, AKM Shahariar Azad Rabby, and Sayed Akhter Hossain. “Ishara-lipi: The first complete multipurposeopen access dataset of isolated characters for bangla sign language.” In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–4. IEEE, 2018.IslamMd SanzidulMousumiSadia Sultana SharminJessanNazmul A.ShahariarAKMRabbyAzadHossainSayed Akhter“Ishara-lipi: The first complete multipurposeopen access dataset of isolated characters for bangla sign language.”In2018 International Conference on Bangla Speech and Language Processing (ICBSLP)14IEEE2018Search in Google Scholar
Asadi-Aghbolaghi, Maryam, Hugo Bertiche, Vicent Roig, Shohreh Kasaei, and Sergio Escalera. “Action recognition from RGB-D data: Comparison and fusion of spatio-temporal handcrafted features and deep strategies.” In Proceedings of the IEEE International conference on computer vision workshops, pp. 3179–3188. 2017.Asadi-AghbolaghiMaryamBerticheHugoRoigVicentKasaeiShohrehEscaleraSergio“Action recognition from RGB-D data: Comparison and fusion of spatio-temporal handcrafted features and deep strategies.”InProceedings of the IEEE International conference on computer vision workshops317931882017Search in Google Scholar
Escalera S, Gonzalez J, Baro X, Reyes M, Lopes O, Guyon I, Athitsos V, Escalante H (2013) Multi-modal gesture recognition challenge 2013: dataset and results, In Proceedings of the 15th ACM on International conference on multimodal interaction, 445–452EscaleraSGonzalezJBaroXReyesMLopesOGuyonIAthitsosVEscalanteH2013Multi-modal gesture recognition challenge 2013: dataset and resultsIn Proceedings of the 15th ACM on International conference on multimodal interaction445452Search in Google Scholar
Cerna, Lourdes Ramirez, Edwin Escobedo Cardenas, Dayse Garcia Miranda, David Menotti, and Guillermo Camara-Chavez. “A multimodal LIBRAS-UFOP Brazilian sign language dataset of minimal pairs using a microsoft Kinect sensor.” Expert Systems with Applications 167 (2021): 114179.]CernaLourdes RamirezCardenasEdwin EscobedoMirandaDayse GarciaMenottiDavidCamara-ChavezGuillermo“A multimodal LIBRAS-UFOP Brazilian sign language dataset of minimal pairs using a microsoft Kinect sensor.”Expert Systems with Applications1672021114179Search in Google Scholar
Sincan, Ozge Mercanoglu, and Hacer Yalim Keles. “Autsl: A large scale multi-modal turkish sign language dataset and baseline methods.” IEEE Access 8 (2020): 181340–181355.SincanOzge MercanogluKelesHacer Yalim“Autsl: A large scale multi-modal turkish sign language dataset and baseline methods.”IEEE Access82020181340181355Search in Google Scholar
Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. “Hand sign language recognition using multi-view hand skeleton.” Expert Systems with Applications 150 (2020): 113336.RastgooRaziehKianiKouroshEscaleraSergio“Hand sign language recognition using multi-view hand skeleton.”Expert Systems with Applications1502020113336Search in Google Scholar
Ronchetti, Franco, Facundo Quiroga, César Armando Estrebou, Laura Cristina Lanzarini, and Alejandro Rosete. “LSA64: an Argentinian sign language dataset.” In XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016). 2016.RonchettiFrancoQuirogaFacundoEstrebouCésar ArmandoLanzariniLaura CristinaRoseteAlejandro“LSA64: an Argentinian sign language dataset.”In XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016)2016Search in Google Scholar
Efthimiou, Eleni, Kiki Vasilaki, Stavroula-Evita Fotinea, Anna Vacalopoulou, Theodoros Goulas, and Athanasia-Lida Dimou. “The POLYTROPON parallel corpus.” In sign-lang@ LREC 2018, pp. 39–44. European Language Resources Association (ELRA), 2018.EfthimiouEleniVasilakiKikiFotineaStavroula-EvitaVacalopoulouAnnaGoulasTheodorosDimouAthanasia-Lida“The POLYTROPON parallel corpus.”Insign-lang@ LREC 20183944European Language Resources Association (ELRA)2018Search in Google Scholar
Ko, Sang-Ki, Chang Jo Kim, Hyedong Jung, and Choongsang Cho. “Neural sign language translation based on human keypoint estimation.” Applied sciences 9, no. 13 (2019): 2683.KoSang-KiKimChang JoJungHyedongChoChoongsang“Neural sign language translation based on human keypoint estimation.”Applied sciences91320192683Search in Google Scholar
Luqman, Hamzah, and Sabri A. Mahmoud. “A machine translation system from Arabic sign language to Arabic.” Universal Access in the Information Society 19, no. 4 (2020): 891–904.LuqmanHamzahMahmoudSabri A.“A machine translation system from Arabic sign language to Arabic.”Universal Access in the Information Society1942020891904Search in Google Scholar
Ruffieux, Simon, Denis Lalanne, Elena Mugellini, and Omar Abou Khaled. “A survey of datasets for human gesture recognition.” In Human-Computer Interaction. Advanced Interaction Modalities and Techniques: 16th International Conference, HCI International 2014, Heraklion, Crete, Greece, June 22–27, 2014, Proceedings, Part II 16, pp. 337–348. Springer International Publishing, 2014.RuffieuxSimonLalanneDenisMugelliniElenaKhaledOmar Abou“A survey of datasets for human gesture recognition.”InHuman-Computer Interaction. Advanced Interaction Modalities and Techniques: 16th International Conference, HCI International 2014Heraklion, Crete, GreeceJune 22–27, 2014Proceedings, Part II 16337348Springer International Publishing2014Search in Google Scholar
Boulahia, Said Yacine, Eric Anquetil, Franck Multon, and Richard Kulpa. “Dynamic hand gesture recognition based on 3D pattern assembled trajectories.” In 2017 seventh international conference on image processing theory, tools and applications (IPTA), pp. 1–6. IEEE, 2017.BoulahiaSaid YacineAnquetilEricMultonFranckKulpaRichard“Dynamic hand gesture recognition based on 3D pattern assembled trajectories.”In2017 seventh international conference on image processing theory, tools and applications (IPTA)16IEEE2017Search in Google Scholar
Avola, Danilo, Marco Bernardi, Luigi Cinque, Gian Luca Foresti, and C. Massaroni. “Exploiting recurrent neural networks and leap motion controller for sign language and semaphoric gesture recognition.” arXiv preprint arXiv:1803.10435.AvolaDaniloBernardiMarcoCinqueLuigiForestiGian LucaMassaroniC.“Exploiting recurrent neural networks and leap motion controller for sign language and semaphoric gesture recognition.”arXiv preprint arXiv:1803.10435.Search in Google Scholar
Chen, Chen, Roozbeh Jafari, and Nasser Kehtarnavaz. “UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor.” In 2015 IEEE International conference on image processing (ICIP), pp. 168–172. IEEE, 2015.ChenChenJafariRoozbehKehtarnavazNasser“UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor.”In2015 IEEE International conference on image processing (ICIP)168172IEEE2015Search in Google Scholar
S. Singh, S.A. Velastin, H. Ragheb, Muhavi: A multicamera human action video dataset for the evaluation of action recognition methods, in 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, IEEE, 2010, pp. 48–55SinghS.VelastinS.A.RaghebH.Muhavi: A multicamera human action video dataset for the evaluation of action recognition methodsin2010 7th IEEE International Conference on Advanced Video and Signal Based SurveillanceIEEE20104855Search in Google Scholar
Zheng, Jingjing, Zhuolin Jiang, P. Jonathon Phillips, and Rama Chellappa. “Cross-View Action Recognition via a Transferable Dictionary Pair.” In bmvc, vol. 1, no. 2, p. 7. 2012.ZhengJingjingJiangZhuolinPhillipsP. JonathonChellappaRama“Cross-View Action Recognition via a Transferable Dictionary Pair.”In bmvc1272012Search in Google Scholar
L. Gorelick, M. Blank, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes, IEEE Trans. Pattern Anal. Mach. Intell. 29GorelickL.BlankM.ShechtmanE.IraniM.BasriR.Actions as space-time shapesIEEE Trans. Pattern Anal. Mach. Intell.29Search in Google Scholar
A. Shahroudy, J. Liu, T.-T. Ng, G. Wang, Ntu rgb+ d: A large scale dataset for 3d human activity analysis, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1010–1019ShahroudyA.LiuJ.NgT.-T.WangG.Ntu rgb+ d: A large scale dataset for 3d human activity analysisin:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition201610101019Search in Google Scholar
Kim, T-K.; Wong, S-F.; Cipolla, R.: Tensor canonical correlation analysis for action classification. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN (2007)KimT-K.WongS-F.CipollaR.Tensor canonical correlation analysis for action classificationInProc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Minneapolis, MN2007Search in Google Scholar
Zhang, Yi, Chong Wang, Ye Zheng, Jieyu Zhao, Yuqi Li, and Xijiong Xie. “Short-term temporal convolutional networks for dynamic hand gesture recognition.” arXiv preprint arXiv:2001.05833 (2019).ZhangYiWangChongZhengYeZhaoJieyuLiYuqiXieXijiong“Short-term temporal convolutional networks for dynamic hand gesture recognition.”arXiv preprint arXiv:2001.058332019Search in Google Scholar
Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth cameras, In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 1290–1297WangJLiuZWuYYuanJ2012Mining actionlet ensemble for action recognition with depth camerasInComputer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on12901297Search in Google Scholar
Koppula, Hema Swetha, Rudhir Gupta, and Ashutosh Saxena. “Learning human activities and object affordances from rgb-d videos.” The International journal of robotics research 32, no. 8 (2013): 951–970.KoppulaHema SwethaGuptaRudhirSaxenaAshutosh“Learning human activities and object affordances from rgb-d videos.”The International journal of robotics research3282013951970Search in Google Scholar
Müller, Meinard, Tido Röder, Michael Clausen, Bernhard Eberhardt, Björn Krüger, and Andreas Weber. “Mocap database hdm05.” Institut für Informatik II, Universität Bonn 2, no. 7 (2007).]MüllerMeinardRöderTidoClausenMichaelEberhardtBernhardKrügerBjörnWeberAndreas“Mocap database hdm05.”Institut für Informatik II, Universität Bonn272007Search in Google Scholar
Gross, Ralph, and Jianbo Shi. “The cmu motion of body (mobo) database. Robotics Institute.” Pittsburgh, PA (2001).GrossRalphShiJianboThe cmu motion of body (mobo) databaseRobotics InstitutePittsburgh, PA2001Search in Google Scholar
Wan J et al. (2016) ChaLearn looking at people RGB-D isolated and continuous datasets for gesture recognition, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USAWanJ2016ChaLearn looking at people RGB-D isolated and continuous datasets for gesture recognitionIEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Las Vegas, NV, USASearch in Google Scholar
Gupta, P. M. X. Y. S., and K. K. S. T. J. Kautz. “Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks.” In CVPR, vol. 1, no. 2, p. 3. 2016.GuptaP. M. X. Y. S.KautzK. K. S. T. J.“Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks.”InCVPR1232016Search in Google Scholar
Bloom, Victoria, Dimitrios Makris, and Vasileios Argyriou. “G3D: A gaming action dataset and real time action recognition evaluation framework.” In 2012 IEEE Computer society conference on computer vision and pattern recognition workshops, pp. 7–12. IEEE, 2012.BloomVictoriaMakrisDimitriosArgyriouVasileios“G3D: A gaming action dataset and real time action recognition evaluation framework.”In2012 IEEE Computer society conference on computer vision and pattern recognition workshops712IEEE2012Search in Google Scholar
Xia, Lu, Chia-Chih Chen, and Jake K. Aggarwal. “View invariant human action recognition using histograms of 3d joints.” In 2012 IEEE computer society conference on computer vision and pattern recognition workshops, pp. 20–27. IEEE, 2012.XiaLuChenChia-ChihAggarwalJake K.“View invariant human action recognition using histograms of 3d joints.”In2012 IEEE computer society conference on computer vision and pattern recognition workshops2027IEEE2012Search in Google Scholar
Garcia-Hernando, Guillermo, Shanxin Yuan, Seungryul Baek, and Tae-Kyun Kim. “First-person hand action benchmark with rgb-d videos and 3d hand pose annotations.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 409–419. 2018.Garcia-HernandoGuillermoYuanShanxinBaekSeungryulKimTae-Kyun“First-person hand action benchmark with rgb-d videos and 3d hand pose annotations.”InProceedings of the IEEE conference on computer vision and pattern recognition4094192018Search in Google Scholar
Materzynska, Joanna, Guillaume Berger, Ingo Bax, and Roland Memisevic. “The jester dataset: A large-scale video dataset of human gestures.” In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0. 2019.MaterzynskaJoannaBergerGuillaumeBaxIngoMemisevicRoland“The jester dataset: A large-scale video dataset of human gestures.”InProceedings of the IEEE/CVF International Conference on Computer Vision Workshops002019Search in Google Scholar
Zhang, Yifan, Congqi Cao, Jian Cheng, and Hanqing Lu. “Egogesture: a new dataset and benchmark for egocentric hand gesture recognition.” IEEE Transactions on Multimedia 20, no. 5 (2018): 1038–1050.ZhangYifanCaoCongqiChengJianLuHanqing“Egogesture: a new dataset and benchmark for egocentric hand gesture recognition.”IEEE Transactions on Multimedia205201810381050Search in Google Scholar
Pisharady, Pramod Kumar, Prahlad Vadakkepat, and Ai Poh Loh. “Attention based detection and recognition of hand postures against complex backgrounds.” International Journal of Computer Vision 101 (2013): 403–419PisharadyPramod KumarVadakkepatPrahladLohAi Poh“Attention based detection and recognition of hand postures against complex backgrounds.”International Journal of Computer Vision1012013403419Search in Google Scholar
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. “Bleu: a method for automatic evaluation of machine translation.” In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 311–318. 2002.PapineniKishoreRoukosSalimWardToddZhuWei-Jing“Bleu: a method for automatic evaluation of machine translation.”InProceedings of the 40th annual meeting of the Association for Computational Linguistics3113182002Search in Google Scholar
Mann, Wolfgang, Chloe R. Marshall, Kathryn Mason, and Gary Morgan. “The acquisition of sign language: The impact of phonetic complexity on phonology.” Language Learning and Development 6, no. 1 (2010): 60–86.MannWolfgangMarshallChloe R.MasonKathrynMorganGary“The acquisition of sign language: The impact of phonetic complexity on phonology.”Language Learning and Development6120106086Search in Google Scholar
Padden, Carol, Irit Meir, Mark Aronoff, and Wendy Sandler. The grammar of space in two new sign languages. na, 2010.PaddenCarolMeirIritAronoffMarkSandlerWendyThe grammar of space in two new sign languagesna,2010Search in Google Scholar
Lillo-Martin, Diane, and Richard P. Meier. “On the linguistic status of ‘agreement’in sign languages.” (2011): 95–141.Lillo-MartinDianeMeierRichard P.“On the linguistic status of ‘agreement’in sign languages.”201195141Search in Google Scholar
Binder, Marc D., Nobutaka Hirokawa, and Uwe Windhorst, eds. Encyclopedia of neuroscience. Vol. 3166. Berlin, Germany: Springer, 2009.BinderMarc D.HirokawaNobutakaWindhorstUweeds.Encyclopedia of neuroscience3166Berlin, GermanySpringer2009Search in Google Scholar
Chen, Xiang, Xu Zhang, Zhang-Yan Zhao, Ji-Hai Yang, Vuokko Lantz, and Kong-Qiao Wang. “Hand gesture recognition research based on surface EMG sensors and 2D-accelerometers.” In 2007 11th IEEE International Symposium on Wearable Computers, pp. 11–14. IEEE, 2007.ChenXiangZhangXuZhaoZhang-YanYangJi-HaiLantzVuokkoWangKong-Qiao“Hand gesture recognition research based on surface EMG sensors and 2D-accelerometers.”In2007 11th IEEE International Symposium on Wearable Computers1114IEEE2007Search in Google Scholar
Li, Wenguo, Zhizeng Luo, and Xugang Xi. “Movement trajectory recognition of sign language based on optimized dynamic time warping.” Electronics 9, no. 9 (2020): 1400.LiWenguoLuoZhizengXiXugang“Movement trajectory recognition of sign language based on optimized dynamic time warping.”Electronics9920201400Search in Google Scholar
Mino, Ajkel, Mirela Popa, and Alexia Briassouli. “The Effect of Spatial and Temporal Occlusion on Word Level Sign Language Recognition.” In 2022 IEEE International Conference on Image Processing (ICIP), pp. 2686–2690. IEEE, 2022.MinoAjkelPopaMirelaBriassouliAlexia“The Effect of Spatial and Temporal Occlusion on Word Level Sign Language Recognition.”In2022 IEEE International Conference on Image Processing (ICIP)26862690IEEE2022Search in Google Scholar
Aran, Oya. “Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components.” Bogazi» ci University (2008).AranOya“Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components.”Bogazi» ci University2008Search in Google Scholar
KaewTraKulPong, Pakorn, and Richard Bowden. “An improved adaptive background mixture model for real-time tracking with shadow detection.” Video-based surveillance systems: Computer vision and distributed processing (2002): 135–144.KaewTraKulPongPakornBowdenRichard“An improved adaptive background mixture model for real-time tracking with shadow detection.”Video-based surveillance systems: Computer vision and distributed processing2002135144Search in Google Scholar
Kakumanu, Praveen, Sokratis Makrogiannis, and Nikolaos Bourbakis. “A survey of skin-color modeling and detection methods.” Pattern recognition 40, no. 3 (2007): 1106–1122.KakumanuPraveenMakrogiannisSokratisBourbakisNikolaos“A survey of skin-color modeling and detection methods.”Pattern recognition403200711061122Search in Google Scholar
Yun, Liu, Zhang Lifeng, and Zhang Shujun. “A hand gesture recognition method based on multi-feature fusion and template matching.” Procedia Engineering 29 (2012): 1678–1684.YunLiuLifengZhangShujunZhang“A hand gesture recognition method based on multi-feature fusion and template matching.”Procedia Engineering29201216781684Search in Google Scholar
Kartika, Dyah Rahma, and Riyanto Sigit. “Sign language interpreter hand using optical flow.” In 2016 International Seminar on Application for Technology of Information and Communication (ISemantic), pp. 197–201. IEEE, 2016.KartikaDyah RahmaSigitRiyanto“Sign language interpreter hand using optical flow.”In2016 International Seminar on Application for Technology of Information and Communication (ISemantic)197201IEEE2016Search in Google Scholar
Neverova, Natalia, Christian Wolf, Graham W. Taylor, and Florian Nebout. “Hand segmentation with structured convolutional learning.” In Computer Vision--ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1–5, 2014, Revised Selected Papers, Part III 12, pp. 687–702. Springer International Publishing, 2015.NeverovaNataliaWolfChristianTaylorGraham W.NeboutFlorian“Hand segmentation with structured convolutional learning.”InComputer Vision--ACCV 2014: 12th Asian Conference on Computer VisionSingapore, SingaporeNovember 1–5, 2014Revised Selected Papers, Part III 12687702Springer International Publishing2015Search in Google Scholar
Tyagi, Akansha, and Sandhya Bansal. “Feature extraction technique for vision-based indian sign language recognition system: A review.” Computational Methods and Data Engineering: Proceedings of ICMDE 2020, Volume 1 (2020): 39–53.TyagiAkanshaBansalSandhya“Feature extraction technique for vision-based indian sign language recognition system: A review.”Computational Methods and Data Engineering: Proceedings of ICMDE 2020, Volume120203953Search in Google Scholar
Shanableh, Tamer, Khaled Assaleh, and Mohammad Al-Rousan. “Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37, no. 3 (2007): 641–650.ShanablehTamerAssalehKhaledAl-RousanMohammad“Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language.”IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)3732007641650Search in Google Scholar
Rice, Leslie, Eric Wong, and Zico Kolter. “Overfitting in adversarially robust deep learning.” In International Conference on Machine Learning, pp. 8093–8104. PMLR, 2020.RiceLeslieWongEricKolterZico“Overfitting in adversarially robust deep learning.”InInternational Conference on Machine Learning80938104PMLR2020Search in Google Scholar
Ying, Xue. “An overview of overfitting and its solutions.” In Journal of physics: Conference series, vol. 1168, p. 022022. IOP Publishing, 2019.YingXue“An overview of overfitting and its solutions.”In Journal of physics: Conference series1168022022IOP Publishing2019Search in Google Scholar
Bisong, Ekaba, and Ekaba Bisong. “Regularization for deep learning.” Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (2019): 415–421.BisongEkabaBisongEkaba“Regularization for deep learning.”Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners2019415421Search in Google Scholar
Srivastava, Nitish, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. “Dropout: a simple way to prevent neural networks from overfitting.” The journal of machine learning research 15, no. 1 (2014): 1929–1958.SrivastavaNitishHintonGeoffreyKrizhevskyAlexSutskeverIlyaSalakhutdinovRuslan“Dropout: a simple way to prevent neural networks from overfitting.”The journal of machine learning research151201419291958Search in Google Scholar
Caruana, Rich, Steve Lawrence, and C. Giles. “Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping.” Advances in neural information processing systems 13 (2000).CaruanaRichLawrenceSteveGilesC.“Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping.”Advances in neural information processing systems132000Search in Google Scholar
Khosla, Cherry, and Baljit Singh Saini. “Enhancing performance of deep learning models with different data augmentation techniques: A survey.” In 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp. 79–85. IEEE, 2020.KhoslaCherrySainiBaljit Singh“Enhancing performance of deep learning models with different data augmentation techniques: A survey.”In2020 International Conference on Intelligent Engineering and Management (ICIEM)7985IEEE2020Search in Google Scholar
Zhang, Chiyuan, Oriol Vinyals, Remi Munos, and Samy Bengio. “A study on overfitting in deep reinforcement learning.” arXiv preprint arXiv:1804.06893 (2018).ZhangChiyuanVinyalsOriolMunosRemiBengioSamy“A study on overfitting in deep reinforcement learning.”arXiv preprint arXiv:1804.068932018Search in Google Scholar
Neyshabur, Behnam, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. “Exploring generalization in deep learning.” Advances in neural information processing systems 30 (2017).NeyshaburBehnamBhojanapalliSrinadhMcAllesterDavidSrebroNati“Exploring generalization in deep learning.”Advances in neural information processing systems302017Search in Google Scholar
Kawaguchi, Kenji, Leslie Pack Kaelbling, and Yoshua Bengio. “Generalization in deep learning.” arXiv preprint arXiv:1710.05468 (2017).KawaguchiKenjiKaelblingLeslie PackBengioYoshua“Generalization in deep learning.”arXiv preprint arXiv:1710.054682017Search in Google Scholar
Hu, Xia, Lingyang Chu, Jian Pei, Weiqing Liu, and Jiang Bian. “Model complexity of deep learning: A survey.” Knowledge and Information Systems 63 (2021): 2585–2619.HuXiaChuLingyangPeiJianLiuWeiqingBianJiang“Model complexity of deep learning: A survey.”Knowledge and Information Systems63202125852619Search in Google Scholar
Tao, Wenjin, Ming C. Leu, and Zhaozheng Yin. “American Sign Language alphabet recognition using Convolutional Neural Networks with multiview augmentation and inference fusion.” Engineering Applications of Artificial Intelligence 76 (2018): 202–213.TaoWenjinLeuMing C.YinZhaozheng“American Sign Language alphabet recognition using Convolutional Neural Networks with multiview augmentation and inference fusion.”Engineering Applications of Artificial Intelligence762018202213Search in Google Scholar
Hossen, M. A., Arun Govindaiah, Sadia Sultana, and Alauddin Bhuiyan. “Bengali sign language recognition using deep convolutional neural network.” In 2018 joint 7th international conference on informatics, electronics & vision (iciev) and 2018 2nd international conference on imaging, vision & pattern recognition (icIVPR), pp. 369–373. IEEE, 2018.HossenM. A.GovindaiahArunSultanaSadiaBhuiyanAlauddin“Bengali sign language recognition using deep convolutional neural network.”In2018 joint 7th international conference on informatics, electronics & vision (iciev) and 2018 2nd international conference on imaging, vision & pattern recognition (icIVPR)369373IEEE2018Search in Google Scholar
Lazo, Cristian, Zaid Sanchez, and Christian del Carpio. “A Static Hand Gesture Recognition for Peruvian Sign Language Using Digital Image Processing and Deep Learning.” In Brazilian Technology Symposium, pp. 281–290. Springer, Cham, 2018.LazoCristianSanchezZaiddel CarpioChristian“A Static Hand Gesture Recognition for Peruvian Sign Language Using Digital Image Processing and Deep Learning.”InBrazilian Technology Symposium281290Springer, Cham2018Search in Google Scholar
Islam, Sanzidul, Sadia Sultana Sharmin Mousumi, AKM Shahariar Azad Rabby, Sayed Akhter Hossain, and Sheikh Abujar. “A potent model to recognize bangla sign language digits using convolutional neural network.” Procedia computer science 143 (2018): 611–618.IslamSanzidulMousumiSadia Sultana SharminAzad RabbyAKM ShahariarHossainSayed AkhterAbujarSheikh“A potent model to recognize bangla sign language digits using convolutional neural network.”Procedia computer science1432018611618Search in Google Scholar
Bao, Peijun, Ana I. Maqueda, Carlos R. del-Blanco, and Narciso García. “Tiny hand gesture recognition without localization via a deep convolutional network.” IEEE Transactions on Consumer Electronics 63, no. 3 (2017): 251–257.BaoPeijunMaquedaAna I.del-BlancoCarlos R.GarcíaNarciso“Tiny hand gesture recognition without localization via a deep convolutional network.”IEEE Transactions on Consumer Electronics6332017251257Search in Google Scholar
Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. “Multi-modal deep hand sign language recognition in still images using restricted Boltzmann machine.” Entropy 20, no. 11 (2018): 809.RastgooRaziehKianiKouroshEscaleraSergio“Multi-modal deep hand sign language recognition in still images using restricted Boltzmann machine.”Entropy20112018809Search in Google Scholar
Amaral, Lucas, Givanildo LN Júnior, Tiago Vieira, and Thales Vieira. “Evaluating deep models for dynamic brazilian sign language recognition.” In Iberoamerican congress on pattern recognition, pp. 930–937. Springer, Cham, 2018.AmaralLucasGivanildoLNJúniorVieiraTiagoVieiraThales“Evaluating deep models for dynamic brazilian sign language recognition.”InIberoamerican congress on pattern recognition930937Springer, Cham2018Search in Google Scholar
Li, Yuan, Xinggang Wang, Wenyu Liu, and Bin Feng. “Deep attention network for joint hand gesture localization and recognition using static RGB-D images.” Information Sciences 441 (2018): 66–78.LiYuanWangXinggangLiuWenyuFengBin“Deep attention network for joint hand gesture localization and recognition using static RGB-D images.”Information Sciences44120186678Search in Google Scholar
Oyedotun, Oyebade K., and Adnan Khashman. “Deep learning in vision-based static hand gesture recognition.” Neural Computing and Applications 28, no. 12 (2017): 3941–3951.OyedotunOyebade K.KhashmanAdnan“Deep learning in vision-based static hand gesture recognition.”Neural Computing and Applications2812201739413951Search in Google Scholar
Ameen, Salem, and Sunil Vadera. “A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images.” Expert Systems 34, no. 3 (2017): e12197.AmeenSalemVaderaSunil“A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images.”Expert Systems3432017e12197Search in Google Scholar
Bheda, Vivek, and Dianna Radpour. “Using deep convolutional networks for gesture recognition in american sign language.” arXiv preprint arXiv:1710.06836 (2017).BhedaVivekRadpourDianna“Using deep convolutional networks for gesture recognition in american sign language.”arXiv preprint arXiv:1710.068362017Search in Google Scholar
Ji, Yangho, Sunmok Kim, Young-Joo Kim, and Ki-Baek Lee. “Human-like sign-language learning method using deep learning.” ETRI Journal 40, no. 4 (2018): 435–445JiYanghoKimSunmokKimYoung-JooLeeKi-Baek“Human-like sign-language learning method using deep learning.”ETRI Journal4042018435445Search in Google Scholar
Pu, Junfu, Wengang Zhou, and Houqiang Li. “Dilated convolutional network with iterative optimization for continuous sign language recognition.” In IJCAI, vol. 3, p. 7. 2018.PuJunfuZhouWengangLiHouqiang“Dilated convolutional network with iterative optimization for continuous sign language recognition.”InIJCAI372018Search in Google Scholar
Daroya, Rangel, Daryl Peralta, and Prospero Naval. “Alphabet sign language image classification using deep learning.” In TENCON 2018-2018 IEEE Region 10 Conference, pp. 0646–0650. IEEE, 2018.DaroyaRangelPeraltaDarylNavalProspero“Alphabet sign language image classification using deep learning.”InTENCON 2018-2018 IEEE Region 10 Conference06460650IEEE2018Search in Google Scholar
Huang, Jie, Wengang Zhou, Houqiang Li, and Weiping Li. “Attention-based 3D-CNNs for large-vocabulary sign language recognition.” IEEE Transactions on Circuits and Systems for Video Technology 29, no. 9 (2018): 2822–2832.HuangJieZhouWengangLiHouqiangLiWeiping“Attention-based 3D-CNNs for large-vocabulary sign language recognition.”IEEE Transactions on Circuits and Systems for Video Technology299201828222832Search in Google Scholar
Chong, Teak-Wei, and Boon-Giin Lee. “American sign language recognition using leap motion controller with machine learning approach.” Sensors 18, no. 10 (2018): 3554.ChongTeak-WeiLeeBoon-Giin“American sign language recognition using leap motion controller with machine learning approach.”Sensors181020183554Search in Google Scholar
Kumar, E. Kiran, P. V. V. Kishore, A. S. C. S. Sastry, M. Teja Kiran Kumar, and D. Anil Kumar. “Training CNNs for 3-D sign language recognition with color texture coded joint angular displacement maps.” IEEE Signal Processing Letters 25, no. 5 (2018): 645–649.KumarE. KiranKishoreP. V. V.SastryA. S. C. S.Teja Kiran KumarM.Anil KumarD.“Training CNNs for 3-D sign language recognition with color texture coded joint angular displacement maps.”IEEE Signal Processing Letters2552018645649Search in Google Scholar
Koller, Oscar, Sepehr Zargaran, Hermann Ney, and Richard Bowden. “Deep sign: Enabling robust statistical continuous sign language recognition via hybrid CNN-HMMs.” International Journal of Computer Vision 126, no. 12 (2018): 1311–1325.KollerOscarZargaranSepehrNeyHermannBowdenRichard“Deep sign: Enabling robust statistical continuous sign language recognition via hybrid CNN-HMMs.”International Journal of Computer Vision12612201813111325Search in Google Scholar
Taskiran, Murat, Mehmet Killioglu, and Nihan Kahraman. “A real-time system for recognition of American sign language by using deep learning.” In 2018 41st international conference on telecommunications and signal processing (TSP), pp. 1–5. IEEE, 2018.TaskiranMuratKilliogluMehmetKahramanNihan“A real-time system for recognition of American sign language by using deep learning.”In2018 41st international conference on telecommunications and signal processing (TSP)15IEEE2018Search in Google Scholar
Shahriar, Shadman, Ashraf Siddiquee, Tanveerul Islam, Abesh Ghosh, Rajat Chakraborty, Asir Intisar Khan, Celia Shahnaz, and Shaikh Anowarul Fattah. “Real-time american sign language recognition using skin segmentation and image category classification with convolutional neural network and deep learning.” In TENCON 2018-2018 IEEE Region 10 Conference, pp. 1168–1171. IEEE, 2018.ShahriarShadmanSiddiqueeAshrafIslamTanveerulGhoshAbeshChakrabortyRajatKhanAsir IntisarShahnazCeliaFattahShaikh Anowarul“Real-time american sign language recognition using skin segmentation and image category classification with convolutional neural network and deep learning.”InTENCON 2018-2018 IEEE Region 10 Conference11681171IEEE2018Search in Google Scholar
Hu, Yong, Hai-Feng Zhao, and Zhi-Gang Wang. “Sign language fingerspelling recognition using depth information and deep belief networks.” International Journal of Pattern Recognition and Artificial Intelligence 32, no. 06 (2018): 1850018.HuYongZhaoHai-FengWangZhi-Gang“Sign language fingerspelling recognition using depth information and deep belief networks.”International Journal of Pattern Recognition and Artificial Intelligence320620181850018Search in Google Scholar
Kishore, P. V. V., G. Anantha Rao, E. Kiran Kumar, M. Teja Kiran Kumar, and D. Anil Kumar. “Selfie sign language recognition with convolutional neural networks.” International Journal of Intelligent Systems and Applications 10, no. 10 (2018): 63.KishoreP. V. V.Anantha RaoG.Kiran KumarE.Teja Kiran KumarM.Anil KumarD.“Selfie sign language recognition with convolutional neural networks.”International Journal of Intelligent Systems and Applications1010201863Search in Google Scholar
Ye, Yuancheng, Yingli Tian, Matt Huenerfauth, and Jingya Liu. “Recognizing american sign language gestures from within continuous videos.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2064–2073. 2018.YeYuanchengTianYingliHuenerfauthMattLiuJingya“Recognizing american sign language gestures from within continuous videos.”InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops206420732018Search in Google Scholar
Avola, Danilo, Marco Bernardi, Luigi Cinque, Gian Luca Foresti, and Cristiano Massaroni. “Exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures.” IEEE Transactions on Multimedia 21, no. 1 (2018): 234–245.AvolaDaniloBernardiMarcoCinqueLuigiForestiGian LucaMassaroniCristiano“Exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures.”IEEE Transactions on Multimedia2112018234245Search in Google Scholar
Ranga, Virender, Nikita Yadav, and Pulkit Garg. “American sign language fingerspelling using hybrid discrete wavelet transform-gabor filter and convolutional neural network.” Journal of Engineering Science and Technology 13, no. 9 (2018): 2655–2669.RangaVirenderYadavNikitaGargPulkit“American sign language fingerspelling using hybrid discrete wavelet transform-gabor filter and convolutional neural network.”Journal of Engineering Science and Technology139201826552669Search in Google Scholar
Vega, AM Rincon, A. Vasquez, W. Amador, and A. Rojas. “Deep learning for the recognition of facial expression in the Colombian sign language.” Annals of Physical and Rehabilitation Medicine 61 (2018): e96.VegaAM RinconVasquezA.AmadorW.RojasA.“Deep learning for the recognition of facial expression in the Colombian sign language.”Annals of Physical and Rehabilitation Medicine612018e96Search in Google Scholar
Suri, Karush, and Rinki Gupta. “Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory.” Computers & Electrical Engineering 78 (2019): 493–503.SuriKarushGuptaRinki“Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory.”Computers & Electrical Engineering782019493503Search in Google Scholar
Huang, Jie, Wengang Zhou, Qilin Zhang, Houqiang Li, and Weiping Li. “Video-based sign language recognition without temporal segmentation.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1. 2018.HuangJieZhouWengangZhangQilinLiHouqiangLiWeiping“Video-based sign language recognition without temporal segmentation.”InProceedings of the AAAI Conference on Artificial Intelligence3212018Search in Google Scholar
Tolentino, Lean Karlo S., RO Serfa Juan, August C. Thio-ac, Maria Abigail B. Pamahoy, Joni Rose R. Forteza, and Xavier Jet O. Garcia. “Static sign language recognition using deep learning.” Int. J. Mach. Learn. Comput 9, no. 6 (2019): 821–827.TolentinoLean Karlo S.Serfa JuanROThio-acAugust C.PamahoyMaria Abigail B.FortezaJoni Rose R.GarciaXavier Jet O.“Static sign language recognition using deep learning.”Int. J. Mach. Learn. Comput962019821827Search in Google Scholar
Pinto, Raimundo F., Carlos DB Borges, Antônio Almeida, and Iális C. Paula. “Static hand gesture recognition based on convolutional neural networks.” Journal of Electrical and Computer Engineering 2019 (2019).PintoRaimundo F.BorgesCarlos DBAlmeidaAntônioPaulaIális C.“Static hand gesture recognition based on convolutional neural networks.”Journal of Electrical and Computer Engineering20192019Search in Google Scholar
Aly, Walaa, Saleh Aly, and Sultan Almotairi. “User-independent American sign language alphabet recognition based on depth image and PCANet features.” IEEE Access 7 (2019): 123138–123150.AlyWalaaAlySalehAlmotairiSultan“User-independent American sign language alphabet recognition based on depth image and PCANet features.”IEEE Access72019123138123150Search in Google Scholar
Joy, Jestin, Kannan Balakrishnan, and M. Sreeraj. “SignQuiz: a quiz-based tool for learning fingerspelled signs in indian sign language using ASLR.” IEEE Access 7 (2019): 28363–28371.JoyJestinBalakrishnanKannanSreerajM.“SignQuiz: a quiz-based tool for learning fingerspelled signs in indian sign language using ASLR.”IEEE Access720192836328371Search in Google Scholar
Cui, Runpeng, Hu Liu, and Changshui Zhang. “A deep neural framework for continuous sign language recognition by iterative training.” IEEE Transactions on Multimedia 21, no. 7 (2019): 1880–1891.CuiRunpengLiuHuZhangChangshui“A deep neural framework for continuous sign language recognition by iterative training.”IEEE Transactions on Multimedia217201918801891Search in Google Scholar
Mittal, Anshul, Pradeep Kumar, Partha Pratim Roy, Raman Balasubramanian, and Bidyut B. Chaudhuri. “A modified LSTM model for continuous sign language recognition using leap motion.” IEEE Sensors Journal 19, no. 16 (2019): 7056–7063.MittalAnshulKumarPradeepRoyPartha PratimBalasubramanianRamanChaudhuriBidyut B.“A modified LSTM model for continuous sign language recognition using leap motion.”IEEE Sensors Journal1916201970567063Search in Google Scholar
Kulhandjian, Hovannes, Prakshi Sharma, Michel Kulhandjian, and Claude D'Amours. “Sign language gesture recognition using doppler radar and deep learning.” In 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE, 2019KulhandjianHovannesSharmaPrakshiKulhandjianMichelD'AmoursClaude“Sign language gesture recognition using doppler radar and deep learning.”In2019 IEEE Globecom Workshops (GC Wkshps)16IEEE2019Search in Google Scholar
Zhang, Shujun, Weijia Meng, Hui Li, and Xuehong Cui. “Multimodal spatiotemporal networks for sign language recognition.” IEEE Access 7 (2019): 180270–180280.ZhangShujunMengWeijiaLiHuiCuiXuehong“Multimodal spatiotemporal networks for sign language recognition.”IEEE Access72019180270180280Search in Google Scholar
Liao, Yanqiu, Pengwen Xiong, Weidong Min, Weiqiong Min, and Jiahao Lu. “Dynamic sign language recognition based on video sequence with BLSTM-3D residual networks.” IEEE Access 7 (2019): 38044–38054.LiaoYanqiuXiongPengwenMinWeidongMinWeiqiongLuJiahao“Dynamic sign language recognition based on video sequence with BLSTM-3D residual networks.”IEEE Access720193804438054Search in Google Scholar
Vo, Anh H., Van-Huy Pham, and Bao T. Nguyen. “Deep learning for vietnamese sign language recognition in video sequence.” International Journal of Machine Learning and Computing 9, no. 4 (2019): 440–445.VoAnh H.PhamVan-HuyNguyenBao T.“Deep learning for vietnamese sign language recognition in video sequence.”International Journal of Machine Learning and Computing942019440445Search in Google Scholar
Liang, Zhi-jie, Sheng-bin Liao, and Bing-zhang Hu. “3D convolutional neural networks for dynamic sign language recognition.” The Computer Journal 61, no. 11 (2018): 1724–1736.LiangZhi-jieLiaoSheng-binHuBing-zhang“3D convolutional neural networks for dynamic sign language recognition.”The Computer Journal6111201817241736Search in Google Scholar
Bhagat, Neel Kamal, Y. Vishnusai, and G. N. Rathna. “Indian sign language gesture recognition using image processing and deep learning.” In 2019 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE, 2019.BhagatNeel KamalVishnusaiY.RathnaG. N.“Indian sign language gesture recognition using image processing and deep learning.”In2019 Digital Image Computing: Techniques and Applications (DICTA)18IEEE2019Search in Google Scholar
Yu, Yi, Xiang Chen, Shuai Cao, Xu Zhang, and Xun Chen. “Exploration of Chinese sign language recognition using wearable sensors based on deep belief net.” IEEE journal of biomedical and health informatics 24, no. 5 (2019): 1310–1320.YuYiChenXiangCaoShuaiZhangXuChenXun“Exploration of Chinese sign language recognition using wearable sensors based on deep belief net.”IEEE journal of biomedical and health informatics245201913101320Search in Google Scholar
Al-Hammadi, Muneer, Ghulam Muhammad, Wadood Abdul, Mansour Alsulaiman, and M. Shamim Hossain. “Hand gesture recognition using 3D-CNN model.” IEEE Consumer Electronics Magazine 9, no. 1 (2019): 95–101.Al-HammadiMuneerMuhammadGhulamAbdulWadoodAlsulaimanMansourHossainM. Shamim“Hand gesture recognition using 3D-CNN model.”IEEE Consumer Electronics Magazine91201995101Search in Google Scholar
Guo, Dan, Wengang Zhou, Anyang Li, Houqiang Li, and Meng Wang. “Hierarchical recurrent deep fusion using adaptive clip summarization for sign language translation.” IEEE Transactions on Image Processing 29 (2019): 1575–1590.GuoDanZhouWengangLiAnyangLiHouqiangWangMeng“Hierarchical recurrent deep fusion using adaptive clip summarization for sign language translation.”IEEE Transactions on Image Processing29201915751590Search in Google Scholar
Kasukurthi, Nikhil, Brij Rokad, Shiv Bidani, and Dr Dennisan. “American Sign Language Alphabet Recognition using Deep Learning.” arXiv preprint arXiv:1905.05487 (2019).KasukurthiNikhilRokadBrijBidaniShivDennisanDr“American Sign Language Alphabet Recognition using Deep Learning.”arXiv preprint arXiv:1905.054872019Search in Google Scholar
Ravi, Sunitha, Maloji Suman, P. V. V. Kishore, Kiran Kumar, and Anil Kumar. “Multi modal spatio temporal co-trained CNNs with single modal testing on RGB–D based sign language gesture recognition.” Journal of Computer Languages 52 (2019): 88–102.RaviSunithaSumanMalojiKishoreP. V. V.KumarKiranKumarAnil“Multi modal spatio temporal co-trained CNNs with single modal testing on RGB–D based sign language gesture recognition.”Journal of Computer Languages52201988102Search in Google Scholar
Ferreira, Pedro M., Diogo Pernes, Ana Rebelo, and Jaime S. Cardoso. “Desire: Deep signer-invariant representations for sign language recognition.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, no. 9 (2019): 5830–5845.FerreiraPedro M.PernesDiogoRebeloAnaCardosoJaime S.“Desire: Deep signer-invariant representations for sign language recognition.”IEEE Transactions on Systems, Man, and Cybernetics: Systems519201958305845Search in Google Scholar
Mazhar, Osama, Benjamin Navarro, Sofiane Ramdani, Robin Passama, and Andrea Cherubini. “A real-time human-robot interaction framework with robust background invariant hand gesture detection.” Robotics and Computer-Integrated Manufacturing 60 (2019): 34–48.MazharOsamaNavarroBenjaminRamdaniSofianePassamaRobinCherubiniAndrea“A real-time human-robot interaction framework with robust background invariant hand gesture detection.”Robotics and Computer-Integrated Manufacturing6020193448Search in Google Scholar
Kamruzzaman, M. M. “Arabic sign language recognition and generating Arabic speech using convolutional neural network.” Wireless Communications and Mobile Computing 2020 (2020).KamruzzamanM. M.“Arabic sign language recognition and generating Arabic speech using convolutional neural network.”Wireless Communications and Mobile Computing20202020Search in Google Scholar
Angona, Tazkia Mim, ASM Siamuzzaman Shaon, Kazi Tahmid Rashad Niloy, Tajbia Karim, Zarin Tasnim, SM Salim Reza, and Tasmima Noushiba Mahbub. “Automated Bangla sign language translation system for alphabets by means of MobileNet.” TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 3 (2020): 1292–1301.AngonaTazkia MimSiamuzzaman ShaonASMNiloyKazi Tahmid RashadKarimTajbiaTasnimZarinSalim RezaSMMahbubTasmima Noushiba“Automated Bangla sign language translation system for alphabets by means of MobileNet.”TELKOMNIKA (Telecommunication Computing Electronics and Control)183202012921301Search in Google Scholar
Elsayed, Eman K., and Doaa R. Fathy. “Sign language semantic translation system using ontology and deep learning.” International Journal of Advanced Computer Science and Applications 11, no. 1 (2020).ElsayedEman K.FathyDoaa R.“Sign language semantic translation system using ontology and deep learning.”International Journal of Advanced Computer Science and Applications1112020Search in Google Scholar
Aly, Saleh, and Walaa Aly. “DeepArSLR: A novel signer-independent deep learning framework for isolated arabic sign language gestures recognition.” IEEE Access 8 (2020): 83199–83212.AlySalehAlyWalaa“DeepArSLR: A novel signer-independent deep learning framework for isolated arabic sign language gestures recognition.”IEEE Access820208319983212Search in Google Scholar
Al-Hammadi, Muneer, Ghulam Muhammad, Wadood Abdul, Mansour Alsulaiman, Mohammed A. Bencherif, Tareq S. Alrayes, Hassan Mathkour, and Mohamed Amine Mekhtiche. “Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation.” IEEE Access 8 (2020): 192527–192542.Al-HammadiMuneerMuhammadGhulamAbdulWadoodAlsulaimanMansourBencherifMohammed A.AlrayesTareq S.MathkourHassanMekhticheMohamed Amine“Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation.”IEEE Access82020192527192542Search in Google Scholar
Latif, Ghazanfar, Nazeeruddin Mohammad, Roaa AlKhalaf, Rawan AlKhalaf, Jaafar Alghazo, and Majid Khan. “An automatic Arabic sign language recognition system based on deep CNN: an assistive system for the deaf and hard of hearing.” International Journal of Computing and Digital Systems 9, no. 4 (2020): 715–724.LatifGhazanfarMohammadNazeeruddinAlKhalafRoaaAlKhalafRawanAlghazoJaafarKhanMajid“An automatic Arabic sign language recognition system based on deep CNN: an assistive system for the deaf and hard of hearing.”International Journal of Computing and Digital Systems942020715724Search in Google Scholar
Al-Hammadi, Muneer, Ghulam Muhammad, Wadood Abdul, Mansour Alsulaiman, Mohamed A. Bencherif, and Mohamed Amine Mekhtiche. “Hand gesture recognition for sign language using 3DCNN.” IEEE Access 8 (2020): 79491–79509.Al-HammadiMuneerMuhammadGhulamAbdulWadoodAlsulaimanMansourBencherifMohamed A.MekhticheMohamed Amine“Hand gesture recognition for sign language using 3DCNN.”IEEE Access820207949179509Search in Google Scholar
Abdulhussein, Abdulwahab A., and Firas A. Raheem. “Hand gesture recognition of static letters american sign language (ASL) using deep learning.” Engineering and Technology Journal 38, no. 6 (2020): 926–937.AbdulhusseinAbdulwahab A.RaheemFiras A.“Hand gesture recognition of static letters american sign language (ASL) using deep learning.”Engineering and Technology Journal3862020926937Search in Google Scholar
Jiang, Xianwei, Mingzhou Lu, and Shui-Hua Wang. “An eight-layer convolutional neural network with stochastic pooling, batch normalization and dropout for fingerspelling recognition of Chinese sign language.” Multimedia Tools and Applications 79, no. 21 (2020): 15697–15715.JiangXianweiLuMingzhouWangShui-Hua“An eight-layer convolutional neural network with stochastic pooling, batch normalization and dropout for fingerspelling recognition of Chinese sign language.”Multimedia Tools and Applications792120201569715715Search in Google Scholar
Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. “Video-based isolated hand sign language recognition using a deep cascaded model.” Multimedia Tools and Applications 79, no. 31 (2020): 22965–22987.RastgooRaziehKianiKouroshEscaleraSergio“Video-based isolated hand sign language recognition using a deep cascaded model.”Multimedia Tools and Applications793120202296522987Search in Google Scholar
Papadimitriou, Katerina, and Gerasimos Potamianos. “Multimodal Sign Language Recognition via Temporal Deformable Convolutional Sequence Learning.” In INTERSPEECH, pp. 2752–2756. 2020.PapadimitriouKaterinaPotamianosGerasimos“Multimodal Sign Language Recognition via Temporal Deformable Convolutional Sequence Learning.”InINTERSPEECH275227562020Search in Google Scholar
Arun, C., and R. Gopikakumari. “Optimisation of both classifier and fusion based feature set for static American sign language recognition.” IET Image Processing 14, no. 10 (2020): 2101–2109.ArunC.GopikakumariR.“Optimisation of both classifier and fusion based feature set for static American sign language recognition.”IET Image Processing1410202021012109Search in Google Scholar
Sabeenian, R. S., S. Sai Bharathwaj, and M. Mohamed Aadhil. “Sign language recognition using deep learning and computer vision.” J. Adv. Res. Dyn. Contr. Syst 12 (2020): 964–968.SabeenianR. S.Sai BharathwajS.Mohamed AadhilM.“Sign language recognition using deep learning and computer vision.”J. Adv. Res. Dyn. Contr. Syst122020964968Search in Google Scholar
Zheng, Jiangbin, Zheng Zhao, Min Chen, Jing Chen, Chong Wu, Yidong Chen, Xiaodong Shi, and Yiqi Tong. “An improved sign language translation model with explainable adaptations for processing long sign sentences.” Computational Intelligence and Neuroscience 2020 (2020).ZhengJiangbinZhaoZhengChenMinChenJingWuChongChenYidongShiXiaodongTongYiqi“An improved sign language translation model with explainable adaptations for processing long sign sentences.”Computational Intelligence and Neuroscience20202020Search in Google Scholar
Jiang, Xianwei, Bo Hu, Suresh Chandra Satapathy, Shui-Hua Wang, and Yu-Dong Zhang. “Fingerspelling identification for Chinese sign language via AlexNet-based transfer learning and Adam optimizer.” Scientific Programming 2020 (2020).JiangXianweiHuBoSatapathySuresh ChandraWangShui-HuaZhangYu-Dong“Fingerspelling identification for Chinese sign language via AlexNet-based transfer learning and Adam optimizer.”Scientific Programming20202020Search in Google Scholar
Ahmed, Hasmath Farhana Thariq, Hafisoh Ahmad, Kulasekharan Narasingamurthi, Houda Harkat, and Swee King Phang. “DF-WiSLR: Device-free Wi-Fi-based sign language recognition.” Pervasive and Mobile Computing 69 (2020): 101289.AhmedHasmath Farhana ThariqAhmadHafisohNarasingamurthiKulasekharanHarkatHoudaPhangSwee King“DF-WiSLR: Device-free Wi-Fi-based sign language recognition.”Pervasive and Mobile Computing692020101289Search in Google Scholar
Parelli, Maria, Katerina Papadimitriou, Gerasimos Potamianos, Georgios Pavlakos, and Petros Maragos. “Exploiting 3d hand pose estimation in deep learning-based sign language recognition from rgb videos.” In European Conference on Computer Vision, pp. 249–263. Springer, Cham, 2020.ParelliMariaPapadimitriouKaterinaPotamianosGerasimosPavlakosGeorgiosMaragosPetros“Exploiting 3d hand pose estimation in deep learning-based sign language recognition from rgb videos.”InEuropean Conference on Computer Vision249263Springer, Cham2020Search in Google Scholar
Park, Chan-Il, and Chae-Bong Sohn. “Data augmentation for human keypoint estimation deep learning based sign language translation.” Electronics 9, no. 8 (2020): 1257.ParkChan-IlSohnChae-Bong“Data augmentation for human keypoint estimation deep learning based sign language translation.”Electronics9820201257Search in Google Scholar
Saleh, Yaser, and Ghassan Issa. “Arabic sign language recognition through deep neural networks fine-tuning.” (2020): 71–83.SalehYaserIssaGhassan“Arabic sign language recognition through deep neural networks fine-tuning.”20207183Search in Google Scholar
Gao, Qinghua, Shuo Jiang, and Peter B. Shull. “Simultaneous hand gesture classification and finger angle estimation via a novel dual-output deep learning model.” Sensors 20, no. 10 (2020): 2972.GaoQinghuaJiangShuoShullPeter B.“Simultaneous hand gesture classification and finger angle estimation via a novel dual-output deep learning model.”Sensors201020202972Search in Google Scholar
Lee, Boon Giin, Teak-Wei Chong, and Wan-Young Chung. “Sensor fusion of motion-based sign language interpretation with deep learning.” Sensors 20, no. 21 (2020): 6256.LeeBoon GiinChongTeak-WeiChungWan-Young“Sensor fusion of motion-based sign language interpretation with deep learning.”Sensors202120206256Search in Google Scholar
Li, Wenguo, Zhizeng Luo, Yan Jin, and Xugang Xi. “Gesture recognition based on multiscale singular value entropy and deep belief network.” Sensors 21, no. 1 (2020): 119.LiWenguoLuoZhizengJinYanXiXugang“Gesture recognition based on multiscale singular value entropy and deep belief network.”Sensors2112020119Search in Google Scholar
Bird, Jordan J., Anikó Ekárt, and Diego R. Faria. “British sign language recognition via late fusion of computer vision and leap motion with transfer learning to american sign language.” Sensors 20, no. 18 (2020): 5151.BirdJordan J.EkártAnikóFariaDiego R.“British sign language recognition via late fusion of computer vision and leap motion with transfer learning to american sign language.”Sensors201820205151Search in Google Scholar
Wang, Zhibo, Tengda Zhao, Jinxin Ma, Hongkai Chen, Kaixin Liu, Huajie Shao, Qian Wang, and Ju Ren. “Hear sign language: A real-time end-to-end sign language recognition system.” IEEE Transactions on Mobile Computing (2020).WangZhiboZhaoTengdaMaJinxinChenHongkaiLiuKaixinShaoHuajieWangQianRenJu“Hear sign language: A real-time end-to-end sign language recognition system.”IEEE Transactions on Mobile Computing2020Search in Google Scholar
Abiyev, Rahib H., Murat Arslan, and John Bush Idoko. “Sign language translation using deep convolutional neural networks.” KSII Transactions on Internet and Information Systems (TIIS) 14, no. 2 (2020): 631–653.AbiyevRahib H.ArslanMuratIdokoJohn Bush“Sign language translation using deep convolutional neural networks.”KSII Transactions on Internet and Information Systems (TIIS)1422020631653Search in Google Scholar
Ojha, Ankit, Ayush Pandey, Shubham Maurya, Abhishek Thakur, and P. Dayananda. “Sign language to text and speech translation in real time using convolutional neural network.” International Journal of Engineering Research & Technology (IJERT) 8, no. 15 (2020).OjhaAnkitPandeyAyushMauryaShubhamThakurAbhishekDayanandaP.“Sign language to text and speech translation in real time using convolutional neural network.”International Journal of Engineering Research & Technology (IJERT)8152020Search in Google Scholar
Adithya, V., and Reghunadhan Rajesh. “A deep convolutional neural network approach for static hand gesture recognition.” Procedia Computer Science 171 (2020): 2353–2361.AdithyaV.RajeshReghunadhan“A deep convolutional neural network approach for static hand gesture recognition.”Procedia Computer Science171202023532361Search in Google Scholar
Kumar, E. Kiran, P. V. V. Kishore, M. Teja Kiran Kumar, and D. Anil Kumar. “3D sign language recognition with joint distance and angular coded color topographical descriptor on a 2–stream CNN.” Neurocomputing 372 (2020): 40–54.KumarE. KiranKishoreP. V. V.Teja Kiran KumarM.Anil KumarD.“3D sign language recognition with joint distance and angular coded color topographical descriptor on a 2–stream CNN.”Neurocomputing37220204054Search in Google Scholar
Cardenas, Edwin Jonathan Escobedo, and Guillermo Camara Chavez. “Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes.” Journal of Visual Communication and Image Representation 71 (2020): 102772.CardenasEdwin Jonathan EscobedoChavezGuillermo Camara“Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes.”Journal of Visual Communication and Image Representation712020102772Search in Google Scholar
Sharma, Prachi, and Radhey Shyam Anand. “A comprehensive evaluation of deep models and optimizers for Indian sign language recognition.” Graphics and Visual Computing 5 (2021): 200032.SharmaPrachiAnandRadhey Shyam“A comprehensive evaluation of deep models and optimizers for Indian sign language recognition.”Graphics and Visual Computing52021200032Search in Google Scholar
Venugopalan, Adithya, and Rajesh Reghunadhan. “Applying deep neural networks for the automatic recognition of sign language words: A communication aid to deaf agriculturists.” Expert Systems with Applications 185 (2021): 115601.VenugopalanAdithyaReghunadhanRajesh“Applying deep neural networks for the automatic recognition of sign language words: A communication aid to deaf agriculturists.”Expert Systems with Applications1852021115601Search in Google Scholar
Sharma, Sakshi, and Sukhwinder Singh. “Vision-based hand gesture recognition using deep learning for the interpretation of sign language.” Expert Systems with Applications 182 (2021): 115657.SharmaSakshiSinghSukhwinder“Vision-based hand gesture recognition using deep learning for the interpretation of sign language.”Expert Systems with Applications1822021115657Search in Google Scholar
Zheng, Jiangbin, Yidong Chen, Chong Wu, Xiaodong Shi, and Suhail Muhammad Kamal. “Enhancing Neural Sign Language Translation by highlighting the facial expression information.” Neurocomputing 464 (2021): 462–472.ZhengJiangbinChenYidongWuChongShiXiaodongKamalSuhail Muhammad“Enhancing Neural Sign Language Translation by highlighting the facial expression information.”Neurocomputing4642021462472Search in Google Scholar
Kulkarni, Aishwarya. “Dynamic sign language translating system using deep learning and natural language processing.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 129–137.KulkarniAishwarya“Dynamic sign language translating system using deep learning and natural language processing.”Turkish Journal of Computer and Mathematics Education (TURCOMAT)12102021129137Search in Google Scholar
Elsayed, Eman K., and Doaa R. Fathy. “Semantic deep learning to translate dynamic sign language.” Int. J. Intell. Eng. Syst 14 (2021).ElsayedEman K.FathyDoaa R.“Semantic deep learning to translate dynamic sign language.”Int. J. Intell. Eng. Syst142021Search in Google Scholar
Amin, Mohamed, Hesahm Hefny, and Mohammed Ammar. “Sign language gloss translation using deep learning models.” International Journal of Advanced Computer Science and Applications 12, no. 11 (2021).AminMohamedHefnyHesahmAmmarMohammed“Sign language gloss translation using deep learning models.”International Journal of Advanced Computer Science and Applications12112021Search in Google Scholar
Martinez-Martin, Ester, and Francisco Morillas-Espejo. “Deep learning techniques for Spanish sign language interpretation.” Computational Intelligence and Neuroscience 2021 (2021).Martinez-MartinEsterMorillas-EspejoFrancisco“Deep learning techniques for Spanish sign language interpretation.”Computational Intelligence and Neuroscience20212021Search in Google Scholar
Park, HyeonJung, Youngki Lee, and JeongGil Ko. “Enabling real-time sign language translation on mobile platforms with on-board depth cameras.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (2021): 1–30.ParkHyeonJungLeeYoungkiKoJeongGil“Enabling real-time sign language translation on mobile platforms with on-board depth cameras.”Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies522021130Search in Google Scholar
Dong, Yongfeng, Jielong Liu, and Wenjie Yan. “Dynamic hand gesture recognition based on signals from specialized data glove and deep learning algorithms.” IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–14.DongYongfengLiuJielongYanWenjie“Dynamic hand gesture recognition based on signals from specialized data glove and deep learning algorithms.”IEEE Transactions on Instrumentation and Measurement702021114Search in Google Scholar
Gauni, Sabitha, Ankit Bastia, B. Sohan Kumar, Prakhar Soni, and Vineeth Pydi. “Translation of Gesture-Based Static Sign Language to Text and Speech.” In Journal of Physics: Conference Series, vol. 1964, no. 6, p. 062074. IOP Publishing, 2021.GauniSabithaBastiaAnkitSohan KumarB.SoniPrakharPydiVineeth“Translation of Gesture-Based Static Sign Language to Text and Speech.”In Journal of Physics: Conference Series19646062074IOP Publishing2021Search in Google Scholar
Aksoy, Bekir, Osamah Khaled Musleh Salman, and Özge Ekrem. “Detection of Turkish Sign Language Using Deep Learning and Image Processing Methods.” Applied Artificial Intelligence 35, no. 12 (2021): 952–981.AksoyBekirSalmanOsamah Khaled MuslehEkremÖzge“Detection of Turkish Sign Language Using Deep Learning and Image Processing Methods.”Applied Artificial Intelligence35122021952981Search in Google Scholar
Barbhuiya, Abul Abbas, Ram Kumar Karsh, and Rahul Jain. “CNN based feature extraction and classification for sign language.” Multimedia Tools and Applications 80, no. 2 (2021): 3051–3069.BarbhuiyaAbul AbbasKarshRam KumarJainRahul“CNN based feature extraction and classification for sign language.”Multimedia Tools and Applications802202130513069Search in Google Scholar
Alam, Md, Mahib Tanvir, Dip Kumar Saha, and Sajal K. Das. “Two-Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation.” SN Computer Science 2, no. 5 (2021): 1–13.AlamMdTanvirMahibSahaDip KumarDasSajal K.“Two-Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation.”SN Computer Science252021113Search in Google Scholar
Wen, Feng, Zixuan Zhang, Tianyiyi He, and Chengkuo Lee. “AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.” Nature communications 12, no. 1 (2021): 1–13.WenFengZhangZixuanHeTianyiyiLeeChengkuo“AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.”Nature communications1212021113Search in Google Scholar
Halvardsson, Gustaf, Johanna Peterson, César Soto-Valero, and Benoit Baudry. “Interpretation of swedish sign language using convolutional neural networks and transfer learning.” SN Computer Science 2, no. 3 (2021): 1–15.HalvardssonGustafPetersonJohannaSoto-ValeroCésarBaudryBenoit“Interpretation of swedish sign language using convolutional neural networks and transfer learning.”SN Computer Science232021115Search in Google Scholar
Fregoso, Jonathan, Claudia I. Gonzalez, and Gabriela E. Martinez. “Optimization of convolutional neural networks architectures using pso for sign language recognition.” Axioms 10, no. 3 (2021): 139.FregosoJonathanGonzalezClaudia I.MartinezGabriela E.“Optimization of convolutional neural networks architectures using pso for sign language recognition.”Axioms1032021139Search in Google Scholar
Wangchuk, Karma, Panomkhawn Riyamongkol, and Rattapoom Waranusast. “Real-time Bhutanese sign language digits recognition system using convolutional neural network.” Ict Express 7, no. 2 (2021): 215–220.WangchukKarmaRiyamongkolPanomkhawnWaranusastRattapoom“Real-time Bhutanese sign language digits recognition system using convolutional neural network.”Ict Express722021215220Search in Google Scholar
Gao, Liqing, Haibo Li, Zhijian Liu, Zekang Liu, Liang Wan, and Wei Feng. “RNN-transducer based Chinese sign language recognition.” Neurocomputing 434 (2021): 45–54.GaoLiqingLiHaiboLiuZhijianLiuZekangWanLiangFengWei“RNN-transducer based Chinese sign language recognition.”Neurocomputing43420214554Search in Google Scholar
Nihal, Ragib Amin, Sejuti Rahman, Nawara Mahmood Broti, and Shamim Ahmed Deowan. “Bangla sign alphabet recognition with zero-shot and transfer learning.” Pattern Recognition Letters 150 (2021): 84–93.NihalRagib AminRahmanSejutiBrotiNawara MahmoodDeowanShamim Ahmed“Bangla sign alphabet recognition with zero-shot and transfer learning.”Pattern Recognition Letters15020218493Search in Google Scholar
Abdul, Wadood, Mansour Alsulaiman, Syed Umar Amin, Mohammed Faisal, Ghulam Muhammad, Fahad R. Albogamy, Mohamed A. Bencherif, and Hamid Ghaleb. “Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM.” Computers and Electrical Engineering 95 (2021): 107395.AbdulWadoodAlsulaimanMansourAminSyed UmarFaisalMohammedMuhammadGhulamAlbogamyFahad R.BencherifMohamed A.GhalebHamid“Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM.”Computers and Electrical Engineering952021107395Search in Google Scholar
Suneetha, M., M. V. D. Prasad, and P. V. V. Kishore. “Multi-view motion modelled deep attention networks (M2DA-Net) for video-based sign language recognition.” Journal of Visual Communication and Image Representation 78 (2021): 103161.SuneethaM.PrasadM. V. D.KishoreP. V. V.“Multi-view motion modelled deep attention networks (M2DA-Net) for video-based sign language recognition.”Journal of Visual Communication and Image Representation782021103161Search in Google Scholar
Breland, Daniel S., Simen B. Skriubakken, Aveen Dayal, Ajit Jha, Phaneendra K. Yalavarthy, and Linga Reddy Cenkeramaddi. “Deep learning-based sign language digits recognition from thermal images with edge computing system.” IEEE Sensors Journal 21, no. 9 (2021): 10445–10453.BrelandDaniel S.SkriubakkenSimen B.DayalAveenJhaAjitYalavarthyPhaneendra K.CenkeramaddiLinga Reddy“Deep learning-based sign language digits recognition from thermal images with edge computing system.”IEEE Sensors Journal21920211044510453Search in Google Scholar
Elakkiya, R., Pandi Vijayakumar, and Neeraj Kumar. “An optimized Generative Adversarial Network based continuous sign language classification.” Expert Systems with Applications 182 (2021): 115276.ElakkiyaR.VijayakumarPandiKumarNeeraj“An optimized Generative Adversarial Network based continuous sign language classification.”Expert Systems with Applications1822021115276Search in Google Scholar
Singh, Dushyant Kumar. “3D-CNN based Dynamic Gesture Recognition for Indian Sign Language Modeling.” Procedia Computer Science 189 (2021): 76–83.SinghDushyant Kumar“3D-CNN based Dynamic Gesture Recognition for Indian Sign Language Modeling.”Procedia Computer Science18920217683Search in Google Scholar
Sharma, Shikhar, and Krishan Kumar. “ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks.” Multimedia Tools and Applications 80, no. 17 (2021): 26319–26331.SharmaShikharKumarKrishan“ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks.”Multimedia Tools and Applications801720212631926331Search in Google Scholar
Lee, Carman KM, Kam KH Ng, Chun-Hsien Chen, Henry CW Lau, S. Y. Chung, and Tiffany Tsoi. “American sign language recognition and training method with recurrent neural network.” Expert Systems with Applications 167 (2021): 114403.LeeCarman KMNgKam KHChenChun-HsienLauHenry CWChungS. Y.TsoiTiffany“American sign language recognition and training method with recurrent neural network.”Expert Systems with Applications1672021114403Search in Google Scholar
Zhou, Zhenxing, Vincent WL Tam, and Edmund Y. Lam. “SignBERT: A BERT-Based Deep Learning Framework for Continuous Sign Language Recognition.” IEEE Access 9 (2021): 161669–161682.ZhouZhenxingTamVincent WLLamEdmund Y.“SignBERT: A BERT-Based Deep Learning Framework for Continuous Sign Language Recognition.”IEEE Access92021161669161682Search in Google Scholar
Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. “Hand pose aware multimodal isolated sign language recognition.” Multimedia Tools and Applications 80, no. 1 (2021): 127–163.RastgooRaziehKianiKouroshEscaleraSergio“Hand pose aware multimodal isolated sign language recognition.”Multimedia Tools and Applications8012021127163Search in Google Scholar
Papastratis, Ilias, Kosmas Dimitropoulos, and Petros Daras. “Continuous sign language recognition through a context-aware generative adversarial network.” Sensors 21, no. 7 (2021): 2437.PapastratisIliasDimitropoulosKosmasDarasPetros“Continuous sign language recognition through a context-aware generative adversarial network.”Sensors21720212437Search in Google Scholar
Jain, Vanita, Achin Jain, Abhinav Chauhan, Srinivasu Soma Kotla, and Ashish Gautam. “American sign language recognition using support vector machine and convolutional neural network.” International Journal of Information Technology 13, no. 3 (2021): 1193–1200.JainVanitaJainAchinChauhanAbhinavKotlaSrinivasu SomaGautamAshish“American sign language recognition using support vector machine and convolutional neural network.”International Journal of Information Technology133202111931200Search in Google Scholar
Alawwad, Rahaf Abdulaziz, Ouiem Bchir, and Mohamed Maher Ben Ismail. “Arabic Sign Language Recognition using Faster R-CNN.” International Journal of Advanced Computer Science and Applications 12, no. 3 (2021).AlawwadRahaf AbdulazizBchirOuiemMaher Ben IsmailMohamed“Arabic Sign Language Recognition using Faster R-CNN.”International Journal of Advanced Computer Science and Applications1232021Search in Google Scholar
Meng, Lu, and Ronghui Li. “An attention-enhanced multi-scale and dual sign language recognition network based on a graph convolution network.” Sensors 21, no. 4 (2021): 1120.MengLuLiRonghui“An attention-enhanced multi-scale and dual sign language recognition network based on a graph convolution network.”Sensors21420211120Search in Google Scholar
Alani, Ali A., and Georgina Cosma. “ArSL-CNN: a convolutional neural network for Arabic sign language gesture recognition.” Indonesian journal of electrical engineering and computer science 22 (2021).AlaniAli A.CosmaGeorgina“ArSL-CNN: a convolutional neural network for Arabic sign language gesture recognition.”Indonesian journal of electrical engineering and computer science222021Search in Google Scholar
Kowdiki, Manisha, and Arti Khaparde. “Adaptive hough transform with optimized deep learning followed by dynamic time warping for hand gesture recognition.” Multimedia Tools and Applications 81, no. 2 (2022): 2095–2126.KowdikiManishaKhapardeArti“Adaptive hough transform with optimized deep learning followed by dynamic time warping for hand gesture recognition.”Multimedia Tools and Applications812202220952126Search in Google Scholar
Mannan, Abdul, Ahmed Abbasi, Abdul Rehman Javed, Anam Ahsan, Thippa Reddy Gadekallu, and Qin Xin. “Hypertuned deep convolutional neural network for sign language recognition.” Computational Intelligence and Neuroscience 2022 (2022).MannanAbdulAbbasiAhmedJavedAbdul RehmanAhsanAnamGadekalluThippa ReddyXinQin“Hypertuned deep convolutional neural network for sign language recognition.”Computational Intelligence and Neuroscience20222022Search in Google Scholar
Balaha, Mostafa Magdy, Sara El-Kady, Hossam Magdy Balaha, Mohamed Salama, Eslam Emad, Muhammed Hassan, and Mahmoud M. Saafan. “A vision-based deep learning approach for independent-users Arabic sign language interpretation.” Multimedia Tools and Applications (2022): 1–20.BalahaMostafa MagdyEl-KadySaraBalahaHossam MagdySalamaMohamedEmadEslamHassanMuhammedSaafanMahmoud M.“A vision-based deep learning approach for independent-users Arabic sign language interpretation.”Multimedia Tools and Applications2022120Search in Google Scholar
Xiao, Hongwang, Yun Yang, Ke Yu, Jiao Tian, Xinyi Cai, Usman Muhammad, and Jinjun Chen. “Sign language digits and alphabets recognition by capsule networks.” Journal of Ambient Intelligence and Humanized Computing 13, no. 4 (2022): 2131–2141.XiaoHongwangYangYunYuKeTianJiaoCaiXinyiMuhammadUsmanChenJinjun“Sign language digits and alphabets recognition by capsule networks.”Journal of Ambient Intelligence and Humanized Computing134202221312141Search in Google Scholar
Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. “Real-time isolated hand sign language recognition using deep networks and SVD.” Journal of Ambient Intelligence and Humanized Computing 13, no. 1 (2022): 591–611.RastgooRaziehKianiKouroshEscaleraSergio“Real-time isolated hand sign language recognition using deep networks and SVD.”Journal of Ambient Intelligence and Humanized Computing1312022591611Search in Google Scholar
Boukdir, Abdelbasset, Mohamed Benaddy, Ayoub Ellahyani, Othmane El Meslouhi, and Mustapha Kardouchi. “Isolated Video-Based Arabic Sign Language Recognition Using Convolutional and Recursive Neural Networks.” Arabian Journal for Science and Engineering 47, no. 2 (2022): 2187–2199.BoukdirAbdelbassetBenaddyMohamedEllahyaniAyoubEl MeslouhiOthmaneKardouchiMustapha“Isolated Video-Based Arabic Sign Language Recognition Using Convolutional and Recursive Neural Networks.”Arabian Journal for Science and Engineering472202221872199Search in Google Scholar
Sharma, Sakshi, and Sukhwinder Singh. “Recognition of Indian sign language (ISL) using deep learning model.” Wireless Personal Communications 123, no. 1 (2022): 671–692.SharmaSakshiSinghSukhwinder“Recognition of Indian sign language (ISL) using deep learning model.”Wireless Personal Communications12312022671692Search in Google Scholar
Rajalakshmi, E., R. Elakkiya, Alexey L. Prikhodko, M. G. Grif, Maxim A. Bakaev, Jatinderkumar R. Saini, Ketan Kotecha, and V. Subramaniyaswamy. “Static and Dynamic Isolated Indian and Russian Sign Language Recognition with Spatial and Temporal Feature Detection Using Hybrid Neural Network.” ACM Transactions on Asian and Low-Resource Language Information Processing 22, no. 1 (2022): 1–23.RajalakshmiE.ElakkiyaR.PrikhodkoAlexey L.GrifM. G.BakaevMaxim A.SainiJatinderkumar R.KotechaKetanSubramaniyaswamyV.“Static and Dynamic Isolated Indian and Russian Sign Language Recognition with Spatial and Temporal Feature Detection Using Hybrid Neural Network.”ACM Transactions on Asian and Low-Resource Language Information Processing2212022123Search in Google Scholar
Nandi, Utpal, Anudyuti Ghorai, Moirangthem Marjit Singh, Chiranjit Changdar, Shubhankar Bhakta, and Rajat Kumar Pal. “Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling.” Multimedia Tools and Applications (2022): 1–22.NandiUtpalGhoraiAnudyutiSinghMoirangthem MarjitChangdarChiranjitBhaktaShubhankarPalRajat Kumar“Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling.”Multimedia Tools and Applications2022122Search in Google Scholar
Miah, Abu Saleh Musa, Jungpil Shin, Md Al Mehedi Hasan, and Md Abdur Rahim. “BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network.” Applied Sciences 12, no. 8 (2022): 3933.MiahAbu Saleh MusaShinJungpilHasanMd Al MehediRahimMd Abdur“BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network.”Applied Sciences12820223933Search in Google Scholar
Duwairi, Rehab Mustafa, and Zain Abdullah Halloush. “Automatic recognition of Arabic alphabets sign language using deep learning.” International Journal of Electrical & Computer Engineering (2088–8708) 12, no. 3 (2022).DuwairiRehab MustafaHalloushZain Abdullah“Automatic recognition of Arabic alphabets sign language using deep learning.”International Journal of Electrical & Computer Engineering (2088–8708)1232022Search in Google Scholar
Musthafa, Najla, and C. G. Raji. “Real time Indian sign language recognition system.” Materials Today: Proceedings 58 (2022): 504–508.MusthafaNajlaRajiC. G.“Real time Indian sign language recognition system.”Materials Today: Proceedings582022504508Search in Google Scholar
Kasapbaşi, Ahmed, Ahmed Eltayeb AHMED ELBUSHRA, AL-HARDANEE Omar, and Arif Yilmaz. “DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals.” Computer Methods and Programs in Biomedicine Update 2 (2022): 100048.KasapbaşiAhmedAHMED ELBUSHRAAhmed EltayebAL-HARDANEEOmarYilmazArif“DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals.”Computer Methods and Programs in Biomedicine Update22022100048Search in Google Scholar
AlKhuraym, Batool Yahya, Mohamed Maher Ben Ismail, and Ouiem Bchir. “Arabic Sign Language Recognition using Lightweight CNN-based Architecture.” International Journal of Advanced Computer Science and Applications 13, no. 4 (2022).AlKhuraymBatool YahyaMaher Ben IsmailMohamedBchirOuiem“Arabic Sign Language Recognition using Lightweight CNN-based Architecture.”International Journal of Advanced Computer Science and Applications1342022Search in Google Scholar
Ismail, Mohammad H., Shefa A. Dawwd, and Fakhrulddin H. Ali. “Dynamic hand gesture recognition of Arabic sign language by using deep convolutional neural networks.” Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 952–962.IsmailMohammad H.DawwdShefa A.AliFakhrulddin H.“Dynamic hand gesture recognition of Arabic sign language by using deep convolutional neural networks.”Indonesian Journal of Electrical Engineering and Computer Science2522022952962Search in Google Scholar
Venugopalan, Adithya, and Rajesh Reghunadhan. “Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients.” Arabian Journal for Science and Engineering (2022): 1–14.VenugopalanAdithyaReghunadhanRajesh“Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients.”Arabian Journal for Science and Engineering2022114Search in Google Scholar
Tyagi, Akansha, and Sandhya Bansal. “Hybrid FiST_CNN approach for feature extraction for vision-based indian sign language recognition.” Int. Arab J. Inf. Technol. 19, no. 3 (2022): 403–411.TyagiAkanshaBansalSandhya“Hybrid FiST_CNN approach for feature extraction for vision-based indian sign language recognition.”Int. Arab J. Inf. Technol.1932022403411Search in Google Scholar
Kothadiya, Deep, Chintan Bhatt, Krenil Sapariya, Kevin Patel, Ana-Belén Gil-González, and Juan M. Corchado. “Deepsign: Sign Language Detection and Recognition Using Deep Learning.” Electronics 11, no. 11 (2022): 1780.KothadiyaDeepBhattChintanSapariyaKrenilPatelKevinGil-GonzálezAna-BelénCorchadoJuan M.“Deepsign: Sign Language Detection and Recognition Using Deep Learning.”Electronics111120221780Search in Google Scholar
Alsaadi, Zaran, Easa Alshamani, Mohammed Alrehaili, Abdulmajeed Ayesh D. Alrashdi, Saleh Albelwi, and Abdelrahman Osman Elfaki. “A Real Time Arabic Sign Language Alphabets (ArSLA) Recognition Model Using Deep Learning Architecture.” Computers 11, no. 5 (2022): 78.AlsaadiZaranAlshamaniEasaAlrehailiMohammedAlrashdiAbdulmajeed Ayesh D.AlbelwiSalehElfakiAbdelrahman Osman“A Real Time Arabic Sign Language Alphabets (ArSLA) Recognition Model Using Deep Learning Architecture.”Computers115202278Search in Google Scholar
Zhou, Zhenxing, Vincent WL Tam, and Edmund Y. Lam. “A Portable Sign Language Collection and Translation Platform with Smart Watches Using a BLSTM-Based Multi-Feature Framework.” Micromachines 13, no. 2 (2022): 333.ZhouZhenxingTamVincent WLLamEdmund Y.“A Portable Sign Language Collection and Translation Platform with Smart Watches Using a BLSTM-Based Multi-Feature Framework.”Micromachines1322022333Search in Google Scholar
Sharma, Shikhar, Krishan Kumar, and Navjot Singh. “Deep eigen space based ASL recognition system.” IETE Journal of Research 68, no. 5 (2022): 3798–3808.SharmaShikharKumarKrishanSinghNavjot“Deep eigen space based ASL recognition system.”IETE Journal of Research685202237983808Search in Google Scholar
Samaan, Gerges H., Abanoub R. Wadie, Abanoub K. Attia, Abanoub M. Asaad, Andrew E. Kamel, Salwa O. Slim, Mohamed S. Abdallah, and Young-Im Cho. “MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition.” Electronics 11, no. 19 (2022): 3228.SamaanGerges H.WadieAbanoub R.AttiaAbanoub K.AsaadAbanoub M.KamelAndrew E.SlimSalwa O.AbdallahMohamed S.ChoYoung-Im“MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition.”Electronics111920223228Search in Google Scholar
Abdullahi, Sunusi Bala, and Kosin Chamnongthai. “American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM.” Sensors 22, no. 4 (2022): 1406.AbdullahiSunusi BalaChamnongthaiKosin“American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM.”Sensors22420221406Search in Google Scholar
Sincan, Ozge Mercanoglu, and Hacer Yalim Keles. “Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition.” IEEE Access 10 (2022): 18608–18618.SincanOzge MercanogluKelesHacer Yalim“Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition.”IEEE Access1020221860818618Search in Google Scholar
Podder, Kanchon Kanti, Muhammad EH Chowdhury, Anas M. Tahir, Zaid Bin Mahbub, Amith Khandakar, Md Shafayet Hossain, and Muhammad Abdul Kadir. “Bangla sign language (bdsl) alphabets and numerals classification using a deep learning model.” Sensors 22, no. 2 (2022): 574.PodderKanchon KantiChowdhuryMuhammad EHTahirAnas M.MahbubZaid BinKhandakarAmithHossainMd ShafayetKadirMuhammad Abdul“Bangla sign language (bdsl) alphabets and numerals classification using a deep learning model.”Sensors2222022574Search in Google Scholar
Luqman, Hamzah. “An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion.” IEEE Access 10 (2022): 93785–93798.LuqmanHamzah“An Efficient Two-Stream Network for Isolated Sign Language Recognition Using Accumulative Video Motion.”IEEE Access1020229378593798Search in Google Scholar
Han, Xiangzu, Fei Lu, Jianqin Yin, Guohui Tian, and Jun Liu. “Sign Language Recognition Based on R (2+1) D With Spatial–Temporal–Channel Attention.” IEEE Transactions on Human-Machine Systems (2022).HanXiangzuLuFeiYinJianqinTianGuohuiLiuJun“Sign Language Recognition Based on R (2+1) D With Spatial–Temporal–Channel Attention.”IEEE Transactions on Human-Machine Systems2022Search in Google Scholar
Sahoo, Jaya Prakash, Allam Jaya Prakash, Paweł Pławiak, and Saunak Samantray. “Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network.” Sensors 22, no. 3 (2022): 706.SahooJaya PrakashJaya PrakashAllamPławiakPawełSamantraySaunak“Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network.”Sensors2232022706Search in Google Scholar
Yirtici, Tolga, and Kamil Yurtkan. “Regional-CNN-based enhanced Turkish sign language recognition.” Signal, Image and Video Processing (2022): 1–7.YirticiTolgaYurtkanKamil“Regional-CNN-based enhanced Turkish sign language recognition.”Signal, Image and Video Processing202217Search in Google Scholar
Katoch, Shagun, Varsha Singh, and Uma Shanker Tiwary. “Indian Sign Language recognition system using SURF with SVM and CNN.” Array 14 (2022): 100141.KatochShagunSinghVarshaTiwaryUma Shanker“Indian Sign Language recognition system using SURF with SVM and CNN.”Array142022100141Search in Google Scholar
Abdullahi, Sunusi Bala, and Kosin Chamnongthai. “American Sign Language Words Recognition using Spatio-Temporal Prosodic and Angle Features: A sequential learning approach.” IEEE Access 10 (2022): 15911–15923.AbdullahiSunusi BalaChamnongthaiKosin“American Sign Language Words Recognition using Spatio-Temporal Prosodic and Angle Features: A sequential learning approach.”IEEE Access1020221591115923Search in Google Scholar
Zhang, Nengbo, Jin Zhang, Yao Ying, Chengwen Luo, and Jianqiang Li. “Wi-Phrase: Deep Residual-MultiHead Model for WiFi Sign Language Phrase Recognition.” IEEE Internet of Things Journal (2022).ZhangNengboZhangJinYingYaoLuoChengwenLiJianqiang“Wi-Phrase: Deep Residual-MultiHead Model for WiFi Sign Language Phrase Recognition.”IEEE Internet of Things Journal2022Search in Google Scholar