Bangla handwritten numeral recognition using convolutional neural network

• Main Authors: Akhand, M. A. H, Rahman, Md. Mahbubar, Shill, P. C., Islam, Shahidul, Rahman, M.M. Hafizur
• Format: Conference or Workshop Item
• Language: English; English
• Published: IEEE 2015
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://irep.iium.edu.my/44250/7/44250-Bangla_handwritten_numeral_recognition_using_convolutional_neural_network_Fullpaper.pdf; http://irep.iium.edu.my/44250/10/44250_Bangla%20handwritten%20numeral%20recognition_Scopus.pdf; http://irep.iium.edu.my/44250/; http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7307467&tag=1

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods.