Effectiveness of artificial neural networks in recognising handwriting characters

Artificial neural networks are one of the tools of modern text recognising systems from images, including handwritten ones. The article presents the results of a computational experiment aimed at analyzing the quality of recognition of handwritten digits by two artificial neural networks (ANNs) wit...

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Main Authors: Marek Miłosz, Janusz Gazda
Format: Article
Language:English
Published: Lublin University of Technology 2018-09-01
Series:Journal of Computer Sciences Institute
Subjects:
Online Access:https://ph.pollub.pl/index.php/jcsi/article/view/680
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spelling doaj-f726119d901646ebb4835fd98a0875e52020-11-25T04:09:11ZengLublin University of TechnologyJournal of Computer Sciences Institute2544-07642018-09-01710.35784/jcsi.680Effectiveness of artificial neural networks in recognising handwriting charactersMarek Miłosz0 Janusz Gazda1Institute of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, PolandInstitute of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland Artificial neural networks are one of the tools of modern text recognising systems from images, including handwritten ones. The article presents the results of a computational experiment aimed at analyzing the quality of recognition of handwritten digits by two artificial neural networks (ANNs) with different architecture and parameters. The correctness indicator was used as the basic criterion for the quality of character recognition. In addition, the number of neurons and their layers and the ANNs learning time were analyzed. The Python language and the TensorFlow library were used to create the ANNs, and software for their learning and testing. Both ANNs were learned and tested using the same big sets of images of handwritten characters. https://ph.pollub.pl/index.php/jcsi/article/view/680character recognition; handwriting; artificial neural networks
collection DOAJ
language English
format Article
sources DOAJ
author Marek Miłosz
Janusz Gazda
spellingShingle Marek Miłosz
Janusz Gazda
Effectiveness of artificial neural networks in recognising handwriting characters
Journal of Computer Sciences Institute
character recognition; handwriting; artificial neural networks
author_facet Marek Miłosz
Janusz Gazda
author_sort Marek Miłosz
title Effectiveness of artificial neural networks in recognising handwriting characters
title_short Effectiveness of artificial neural networks in recognising handwriting characters
title_full Effectiveness of artificial neural networks in recognising handwriting characters
title_fullStr Effectiveness of artificial neural networks in recognising handwriting characters
title_full_unstemmed Effectiveness of artificial neural networks in recognising handwriting characters
title_sort effectiveness of artificial neural networks in recognising handwriting characters
publisher Lublin University of Technology
series Journal of Computer Sciences Institute
issn 2544-0764
publishDate 2018-09-01
description Artificial neural networks are one of the tools of modern text recognising systems from images, including handwritten ones. The article presents the results of a computational experiment aimed at analyzing the quality of recognition of handwritten digits by two artificial neural networks (ANNs) with different architecture and parameters. The correctness indicator was used as the basic criterion for the quality of character recognition. In addition, the number of neurons and their layers and the ANNs learning time were analyzed. The Python language and the TensorFlow library were used to create the ANNs, and software for their learning and testing. Both ANNs were learned and tested using the same big sets of images of handwritten characters.
topic character recognition; handwriting; artificial neural networks
url https://ph.pollub.pl/index.php/jcsi/article/view/680
work_keys_str_mv AT marekmiłosz effectivenessofartificialneuralnetworksinrecognisinghandwritingcharacters
AT januszgazda effectivenessofartificialneuralnetworksinrecognisinghandwritingcharacters
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