<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Handwriting Recognition (HWR) is a difficult and varied discipline having applications in a wide range of fields, including banking, education, and administration. This research investigates the two main types of HWR systems: online and offline character recognition. Online HWR entails real-time input utilizing digital pens to capture dynamic handwriting traits. It’s used in contemporary gadgets like tablet computers and for signature verification. Offline HWR, on the other hand, processes scanned documents, making it important in situations such as bank cheque processing and assisting the visually handicapped. The research emphasizes the continuing potential for progress in HWR, notably using machine learning and deep learning approaches. Machine learning, a subset of Artificial Intelligence (AI), is critical in developing character recognition algorithms. The selection of an effective classification model is a vital decision, and the study uses a specific dataset to conduct a comparison analysis of alternative models to help in this process. Such assessments provide useful insights for academics and practitioners, allowing them to make more informed judgements on model development for HWR applications.</p>
</abstract>

Handwriting Recognition (HWR) is a difficult and varied discipline having applications in a wide range of fields, including banking, education, and administration. This research investigates the two main types of HWR systems: online and offline character recognition. Online HWR entails real-time input utilizing digital pens to capture dynamic handwriting traits. It’s used in contemporary gadgets like tablet computers and for signature verification. Offline HWR, on the other hand, processes scanned documents, making it important in situations such as bank cheque processing and assisting the visually handicapped. The research emphasizes the continuing potential for progress in HWR, notably using machine learning and deep learning approaches. Machine learning, a subset of Artificial Intelligence (AI), is critical in developing character recognition algorithms. The selection of an effective classification model is a vital decision, and the study uses a specific dataset to conduct a comparison analysis of alternative models to help in this process. Such assessments provide useful insights for academics and practitioners, allowing them to make more informed judgements on model development for HWR applications.

Enhancing Handwritten Alphabet Prediction with Real-time IoT Sensor Integration in Machine Learning for Image

Bharati Vidyapeeth’s College of Engineering Paschim Vihar, Bvs University

National University of Computer and Emerging Sciences

Department of Computer Science & Engineering

Journal of Smart Internet of Things

This work is licensed under the Creative Commons Attribution 4.0 International License.

Handwriting Recognition (HWR) is a difficult and varied discipline having applications in a wide range of fields, including banking, education, and...

Enhancing Handwritten Alphabet Prediction with Real-time IoT Sensor Integration in Machine Learning for Image

Online veröffentlicht: 15. Dez. 2022

Seitenbereich: 53 - 64

Eingereicht: 20. Aug. 2023

Akzeptiert: 21. Okt. 2023

DOI: https://doi.org/10.2478/jsiot-2022-0004

Schlüsselwörter
Handwriting Recognition (HWR), Machine learning, Neural Networks, Support Vector Machines, Naive Bayes

© 2022 Rohan Gautam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Enhancing Handwritten Alphabet Prediction with Real-time IoT Sensor Integration in Machine Learning for Image

Online veröffentlicht: 15. Dez. 2022

Seitenbereich: 53 - 64

Eingereicht: 20. Aug. 2023

Akzeptiert: 21. Okt. 2023

DOI: https://doi.org/10.2478/jsiot-2022-0004

SchlüsselwörterHandwriting Recognition (HWR), Machine learning, Neural Networks, Support Vector Machines, Naive Bayes

© 2022 Rohan Gautam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Handwriting Recognition (HWR), Machine learning, Neural Networks, Support Vector Machines, Naive Bayes