<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper we implement six different learning algorithms in Optical Character Recognition (OCR) problem and achieve the criteria of end-time, number of iterations, train-set performance, test-set performance, validate-set performance and overall performance of these methods and compare them. Finally, we show the advantages and disadvantages of each method.</p></abstract>

In this paper we implement six different learning algorithms in Optical Character Recognition (OCR) problem and achieve the criteria of end-time, number of iterations, train-set performance, test-set performance, validate-set performance and overall performance of these methods and compare them. Finally, we show the advantages and disadvantages of each method.

Benchmark of Different Gradient Descents in OCR

Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Benchmark of Different Gradient Descents in OCR"}

In this paper we implement six different learning algorithms in Optical Character Recognition (OCR) problem and achieve the criteria of end-time, number of...

Benchmark of Different Gradient Descents in OCR

Publié en ligne: 15 juil. 2014

Pages: 114 - 126

DOI: https://doi.org/10.2478/cait-2014-0024

Mots clés
Learning algorithm, neural network, Optical Character Recognition (OCR), pattern recognition

© 2014 Marjan Kuchaki Rafsanjani and Masoud Pourshaban

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Benchmark of Different Gradient Descents in OCR

Publié en ligne: 15 juil. 2014

Pages: 114 - 126

DOI: https://doi.org/10.2478/cait-2014-0024

Mots clésLearning algorithm, neural network, Optical Character Recognition (OCR), pattern recognition

© 2014 Marjan Kuchaki Rafsanjani and Masoud Pourshaban

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Learning algorithm, neural network, Optical Character Recognition (OCR), pattern recognition