vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffad332c000000851d060001000600 Since, last one-decade, numerous deep learning models have been designed to resolve handwritten character recognition task in languages, namely, English, Chinese, Arabic, Japanese and Russian. Recognition of Bengali handwritten character from document image datasets is undoubtedly an open challenging task. Due to the advancement of neural network, many models have been developed and it is improving performance. The LeNet is a pioneering work in the field handwritten document image recognition specially hand written digits from the images by using CNN. This paper focuses on designing a convolution neural network with refinements on layers and its parameter tuning for Bengali character recognition system for classification of 50 different fonts. Our revised CNN model outperforms on some existing approach and shows font-recognition accuracy of 98.46%.
Shankha De1, Arpana Rawal2
Bhilai Institute of Technology, India1, Bhilai Institute of Technology, India2
Convolution Neural Network, Handwritten Character, LeNet
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 12 , Issue: 2 , Pages: 2545-2550 )
Date of Publication :
January 2022
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