Optical Recognition of Handwritten Logic Formulas Using Neural Networks

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various tr...

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Autores principales:	Vaios Ampelakiotis, Isidoros Perikos, Ioannis Hatzilygeroudis, George Tsihrintzis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optical character recognition logic formulas neural networks resilient propagation OpenCV Encog Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/bac261d9ab684d67a521b64faad04007
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spelling	oai:doaj.org-article:bac261d9ab684d67a521b64faad040072021-11-25T17:24:26ZOptical Recognition of Handwritten Logic Formulas Using Neural Networks10.3390/electronics102227612079-9292https://doaj.org/article/bac261d9ab684d67a521b64faad040072021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2761https://doaj.org/toc/2079-9292In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.Vaios AmpelakiotisIsidoros PerikosIoannis HatzilygeroudisGeorge TsihrintzisMDPI AGarticleoptical character recognitionlogic formulasneural networksresilient propagationOpenCVEncogElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2761, p 2761 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optical character recognition logic formulas neural networks resilient propagation OpenCV Encog Electronics TK7800-8360
spellingShingle	optical character recognition logic formulas neural networks resilient propagation OpenCV Encog Electronics TK7800-8360 Vaios Ampelakiotis Isidoros Perikos Ioannis Hatzilygeroudis George Tsihrintzis Optical Recognition of Handwritten Logic Formulas Using Neural Networks
description	In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.
format	article
author	Vaios Ampelakiotis Isidoros Perikos Ioannis Hatzilygeroudis George Tsihrintzis
author_facet	Vaios Ampelakiotis Isidoros Perikos Ioannis Hatzilygeroudis George Tsihrintzis
author_sort	Vaios Ampelakiotis
title	Optical Recognition of Handwritten Logic Formulas Using Neural Networks
title_short	Optical Recognition of Handwritten Logic Formulas Using Neural Networks
title_full	Optical Recognition of Handwritten Logic Formulas Using Neural Networks
title_fullStr	Optical Recognition of Handwritten Logic Formulas Using Neural Networks
title_full_unstemmed	Optical Recognition of Handwritten Logic Formulas Using Neural Networks
title_sort	optical recognition of handwritten logic formulas using neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bac261d9ab684d67a521b64faad04007
work_keys_str_mv	AT vaiosampelakiotis opticalrecognitionofhandwrittenlogicformulasusingneuralnetworks AT isidorosperikos opticalrecognitionofhandwrittenlogicformulasusingneuralnetworks AT ioannishatzilygeroudis opticalrecognitionofhandwrittenlogicformulasusingneuralnetworks AT georgetsihrintzis opticalrecognitionofhandwrittenlogicformulasusingneuralnetworks
_version_	1718412432955670528

Optical Recognition of Handwritten Logic Formulas Using Neural Networks

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