ICTACT Journal on Soft Computing

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Bangla Handwritten Character Recognition Using Convolution Neural Network


Affiliations
1 Department of Computer Science and Engineering, Bhilai Institute of Technology, India
     

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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%.

Keywords

Convolution Neural Network, Handwritten Character, LeNet
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  • V. Patil and S. Shimpi, “Handwritten English Character Recognition Using Neural Network”, Elixir International Journal: Computer Science and Engineering, Vol. 41, pp. 5587-5591, 2011.

  • Y.C. Wong, L.J. Choi, R.S.S. Singh, H. Zhang and A.R. Syafeeza, “Deep Learning-Based Racing Bib Number Detection and Recognition”, Jordanian Journal of Computers and Information Technology, Vol. 5, No. 3, pp. 181-194, 2019.

  • G. Abandah, M. Khedher and K. Younis, “Evaluating and Selecting Features for Recognizing Handwritten Arabic Characters”, Technical Report, Department of Computer Engineering, The University of Jordan, pp. 1-156, 2007.

  • Idris Alsheikh, Mohd Masnizah and Warlina Lia, “A Review of Arabic Text Recognition Dataset”, Asia-Pacific Journal of Information Technology and Multimedia, Vol. 9, pp. 6981, 2020.

  • Khaled S. Younis, “Arabic Hand-Written Character Recognition Based on Deep Convolutional Neural Networks”, Jordanian Journal of Computers and Information Technology, Vol. 3, No. 3, pp. 1-13, 2017.

  • Ahmad Hasasneh, “Arabic Sign Language Characters Recognition Based on A Deep Learning Approach and a Simple Linear Classifier”, Journal of Computers and Information Technology, Vol. 6, No. 3, pp. 1-15, 2020.

  • F. Yin, Q.F. Wang, X.Y. Zhang and C.L. Liu, “Chinese Handwriting Recognition Competition”, Proceedings of International Conference on Document Analysis and Recognition, pp. 1464-1470, 2013.

  • B. Zhu, X.D. Zhou, C.L. Liu and M. Nakagawa, “A Robust Model for On-Line Handwritten Japanese Text Recognition”, International Journal on Document Analysis and Recognition, Vol. 13, No. 2, pp. 121-131, 2010.

  • Mayur Bhargab Bora, Dinthisrang Daimary, Khwairakpam Amitab and Debdatta Kandar, “Handwritten Character Recognition from Images using CNN-ECOC”, Procedia Computer Science, Vol. 167, pp. 2403-2409, 2020

  • N. Das, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri and D.K. Basu, “A Genetic Algorithm Based Region Sampling for Selection of Local Features in Handwritten Digit Recognition Application”, Applied Soft Computing, Vol. 12, pp. 1592-1606, 2012.

  • N. Das, J.M. Reddy, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri and D.K. Basu, “A Statistical-Topological Feature Combination for Recognition of Handwritten Numerals”, Applied Soft Computing, Vol. 12, pp. 2486-2495, 2012.

  • N. Das, K. Acharya, R. Sarkar, S. Basu, M. Kundu and M. Nasipuri, “A Novel GA-SVM Based Multistage Approach for Recognition of Handwritten Bangla Compound Characters”, Proceedings of the International Conference on Information Systems Design and Intelligent Applications, pp. 145-152, 2012.

  • N. Das, S. Basu, R. Sarkar, M. Kundu, M. Nasipuri and D.K. Basu, “Handwritten Bangla Compound Character Recognition: Potential Challenges and Probable Solution”, Proceedings of the International Conference on Artificial Intelligence, pp. 1901-1913, 2009.

  • N. Das, S. Basu, R. Sarkar, M. Kundu, M. Nasipuri and D.K. Basu, “An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet”, Proceedings of the International Conference on Signal and Image Processing, pp. 451-454, 2009.

  • N. Das, K. Acharya, R. Sarkar, S. Basu, M. Kundu and M. Nasipuri, “A Benchmark Data Base of Isolated Bangla Handwritten Compound Characters”, International Journal on Document Analysis and Recognition, Vol. 17, pp. 413431, 2014.

  • B.B. Chaudhuri and U. Pal, “A Complete Printed Bangla OCR System”, Pattern Recognition, Vol. 31, pp. 531-549, 1998.

  • U. Pal, A. Belad and C.H. Choisy “Touching Numeral Segmentation using Water Reservoir Concept”, Pattern Recognition Letters, Vol. 24, pp. 261-272, 2003.

  • T.K. Bhowmik, U. Bhattacharya and S.K. Parui, “Recognition of Bangla Handwritten Characters using An MLP Classifier Based on Stroke Features”, Proceedings of the International Conference on Neural Information, pp. 814-819, 2004.

  • S. Basu, N. Das, R. Sarkar and D.K. Basu, “Handwritten Bangla Alphabet Recognition using An MLP Based Classifier”, Proceedings of the International Conference on Artificial Intelligence, pp. 1-8, 2012.

  • M.D. Rahman and P.C. Shill, “Bangla Handwritten Character Recognition using Convolutional Neural Network”, International Journal on Image, Graphics and Signal Processing, Vol. 8, pp. 42-49, 2015.

  • A.K.M.S. Rabby, S. Azad, M.S. Haque and S.A. Hossain, “Bornonet: Bangla Handwritten Characters Recognition using Convolutional Neural Network”, Procedia Computer Science, Vol. 143, pp. 528-535, 2018.

  • A.K.M.S. Rabby, S. Azad, M.S. Haque and S.A. Hossain, “Ekushnet: Using Convolutional Neural Network for Bangla Handwritten Recognition”, Procedia Computer Science, Vol. 143, pp. 603-610, 2018.

  • M.A.I. Rizvi, K. Deb, M.I. Khan, M.M.S. Kowsar and T. Khanam, “A Comparative Study on Handwritten Bangla Character Recognition”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 27, No. 4, pp. 3195-3207, 2019.

  • S. Chatterjee, R.K. Dutta and S. Roy, “Bengali Handwritten Character Classification using Transfer Learning on Deep Convolutional Network”, Proceedings of the International Conference on Intelligent Human Computer Interaction, pp. 138-148, 2019.

  • I. Khandokar, M. Hasan, F. Ernawan, S. Islam and M.N. Kabir, “Handwritten Character Recognition using Convolutional Neural Network”, Journal of Physics: Conference Series, Vol. 1918, No. 4, pp. 593-611, 2021.

  • A. Sayeed, J. Shin and M.M. Hasan, “BengaliNet: A LowCost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition”, Applied Science, Vol. 11, pp. 6845-6854, 2021.

  • Alejandro Baldominos, Yago Saez and Pedro Isasi, “Evolutionary Convolutional Neural Networks: an

  • application to Handwriting Recognition”, Neurocomputing, Vol. 49, pp. 1-16, 2017.

  • S.L. Chooi and Aimi Syamimi Binti A.B. Ghafar, “Handwritten Character Recognition using Convolutional Neural Network”, Progress in Engineering Application and Technology, Vol. 2, No. 1, pp. 593-611, 2021.


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  • Bangla Handwritten Character Recognition Using Convolution Neural Network

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Authors

Shankha De
Department of Computer Science and Engineering, Bhilai Institute of Technology, India
Arpana Rawal
Department of Computer Science and Engineering, Bhilai Institute of Technology, India

Abstract


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%.

Keywords


Convolution Neural Network, Handwritten Character, LeNet

References