Bangla Handwritten Character Recognition using Convolutional Neural Network

Full Text (PDF, 784KB), PP.42-49

Views: 0 Downloads: 0


Md. Mahbubar Rahman 1,* M. A. H. Akhand 1 Shahidul Islam 1 Pintu Chandra Shill 1 M. M. Hafizur Rahman 2

1. Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna, Bangladesh

2. Dept. of Computer Science, KICT, International Islamic University Malaysia Selangor, Malaysia

* Corresponding author.


Received: 5 Mar. 2015 / Revised: 24 Apr. 2015 / Accepted: 9 Jun. 2015 / Published: 8 Jul. 2015

Index Terms

Handwritten Character Recognition, Bangla, Convolutional Neural Network


Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters. Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla. Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition. In this paper, a CNN based Bangla handwritten character recognition is investigated. The proposed method normalizes the written character images and then employ CNN to classify individual characters. It does not employ any feature extraction method like other related works. 20000 handwritten characters with different shapes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.

Cite This Paper

Md. Mahbubar Rahman, M. A. H. Akhand, Shahidul Islam, Pintu Chandra Shill, M. M. Hafizur Rahman,"Bangla Handwritten Character Recognition using Convolutional Neural Network", IJIGSP, vol.7, no.8, pp.42-49, 2015. DOI: 10.5815/ijigsp.2015.08.05


[1]L. F. C. Pessoa and P. Maragos, Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition, Pattern Recognition, vol. 33, no. 6, pp. 945-960, June 2000.

[2]Bellili, M. Gilloux and P. Gallinari, An MLP-SVM combination architecture for offline handwritten digit recognition, Document Analysis and Recognition, Springer-Verlag, vol. 5, no. 4, pp. 244-252, 2003.

[3]J. Dong, A. Krzy┼╝ak and C. Y. Suen, An improved handwritten Chinese character recognition system using support vector machine, Pattern Recognition Letters, vol. 26, no. 12, pp. 1849-1856, September 2005.

[4]G. Vamvakas, B. Gatos and S. J. Perantonis, Handwritten character recognition through two-stage foreground sub-sampling, Pattern Recognition, vol. 43, no. 8, pp. 2807-2816, August 2010.

[5]Y. Lecun and Y. Bengio, Pattern Recognition and Neural Networks, in Arbib, M. A. (Eds), The Handbook of BrainTheory and Neural Networks, MIT Press 1995.

[6]Guyon, L. Schomaker, R. Plamondon, M. Liberman, and S. Janet, Unipen project of on-line data exchange and recognizer benchmarks, in proc. of 12th International. Conference on Pattern Recognition (ICPR), vol. 2, pp. 29–33, IEEE, 1994.

[7]Yuan, G. Bai, L. Jiao and Y. Liu, Offline handwritten English character recognition based on convolutional neural network, in 10th IAPR International Workshop on Document Analysis Systems (DAS), pp. 125-129, doi: 10.1109/DAS.2012.61, 2012.

[8]D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, Convolutional Neural Network Committees for Handwritten Character Classification, International Conference on Document Analysis and Recognition (ICDAR), pp. 1135-1139, doi: 10.1109/ICDAR.2011.229, 2011.

[9]T. K. Bhowmik, P. Ghanty, A. Roy and S. K. Parui, SVM-based hierarchical architec-tures for handwritten Bangla character recognition, International Journal on Document Analysis and Recognition, vol. 12, no. 2, pp. 97-108, 2009.

[10]S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri and D. K. Basu, A hierarchicalapproach to recognition of handwritten Bangla characters, Pattern Recognition, vol. 42, pp. 1467–1484, 2009.

[11]U. Bhattacharya, M. Shridhar, S. K. Parui, P. K. Sen and B. B. Chaudhuri, Offline recognition of handwritten Bangla characters: an efficient two-stage approach, Pattern Analysis and Applications, vol. 15, no. 4 , pp. 445-458, 2012.

[12]Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document Recognition, in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, November 1998.

[13]Feature extraction using convolution. Available: