Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
Mahto, Manoj Kumar (Gurukula Kangri Vishwavidyalaya, Haridwar, U.K., India)
Bhatia, Karamjit (Gurukula Kangri Vishwavidyalaya, Haridwar, U.K., India)
Sharma, Rajendra Kumar (Thapar Institute of Engineering & Technology, Patiala, Punjab, India)

Data:	2021
Resum:	Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98. 5% for characters and 98. 6% for numerals.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Character and text recognition ; Handwritten recognition ; Document analysis
Publicat a:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 20 Núm. 2 (2021) , p. 69-82 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1282
DOI: 10.5565/rev/elcvia.1282

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