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Ashiquzzaman, A. and Tushar, A.K. (2017) Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks. IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Dhaka, 13-14 February 2017, 1-4.
https://doi.org/10.1109/ICIVPR.2017.7890866

has been cited by the following article:

• TITLE: Bangla Handwritten Character Recognition Using Extended Convolutional Neural Network

  AUTHORS: Tandra Rani Das, Sharad Hasan, Md. Rafsan Jani, Fahima Tabassum, Md. Imdadul Islam

  KEYWORDS: Loss and Accuracy, Deep Neural Network, Image Classification, Noise Removal, CNN and HCR

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.3, March 31, 2021

  ABSTRACT: The necessity of recognizing handwritten characters is increasing day by day because of its various applications. The objective of this paper is to provide a sophisticated, effective and efficient way to recognize and classify Bangla handwritten characters. Here an extended convolutional neural network (CNN) model has been proposed to recognize Bangla handwritten characters. Our CNN model is tested on “BanglalLekha-Isolated” dataset where there are 10 classes for digits, 11 classes for vowels and 39 classes for consonants. Our model shows accuracy of recognition as: 99.50% for Bangla digits, 93.18% for vowels, 90.00% for consonants and 92.25% for combined classes.