Abstract
With the increment of computation power, recognizing handwritten Character has become popular and significant improvement has been achieved for most of the major languages. But Bengali character recognition system is not well enough because of the presence of perplexing character and excessive cursive in its characters. Although several research works have been conducted for recognizing the Bengali characters, an efficient procedure is yet to discover. As the number of datasets is inadequate, most of these studies could not achieve a satisfactory level. So we propose here to train a Convolution Neural Network (CNN) and tune the parameters for better accuracy. This procedure is applied to CMATERDB 3.1.2 dataset with 15000.
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References
Kim, I.-J., Xie, X.: Handwritten Hangul recognition using deep convolutional neural networks. IJDAR 18, 1–13 (2014). https://doi.org/10.1007/s10032-014-0229-4
Senior, A., Robinson, A.: An off-line cursive handwriting recognition system. IEEE Trans. Pattern Anal. Mach. Intell. 20, 309–321 (1998)
Kim, D., Bang, S.-Y.: A handwritten numeral character classification using tolerant rough set. IEEE Trans. Pattern Anal. Mach. Intell. 22, 923–937 (2000)
Pal, U., Chaudhuri, B.: OCR in Bangla: an Indo-Bangladeshi language. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5) (1994)
Unsupervised Feature Learning and Deep Learning Tutorial. http://deeplearning.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork
CS231n Convolutional Neural Networks for Visual Recognition. http://cs231n.github.io/convolutional-networks
Kumar, P., Sharma, N., Rana, A.: Handwritten character recognition using different kernel based SVM classifier and MLP neural network (A COMPARISON). Int. J. Comput. Appl. 53, 25–31 (2012)
Das, N., Sarkar, R., Basu, S., Saha, P., Kundu, M., Nasipuri, M.: Handwritten Bangla character recognition using a soft computing paradigm embedded in two pass approach. Pattern Recogn. 48, 2054–2071 (2015)
Rahman, M., Akhand, M., Islam, S., Chandra Shill, P., Hafizur Rahman, M.: Bangla handwritten character recognition using convolutional neural network. Int. J. Image Graph. Sign. Process. 7, 42–49 (2015)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980). https://doi.org/10.1007/BF00344251
Wen, Y., Lu, Y., Shi, P.: Handwritten Bangla numeral recognition system and its application to postal automation. Pattern Recogn. 40, 99–107 (2007)
Dutta, A., Chaudhury, S.: Bengali alpha-numeric character recognition using curvature features. Pattern Recogn. 26, 1757–1770 (1993)
Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., Basu, D.: A hierarchical approach to recognition of handwritten Bangla characters. Pattern Recogn. 42, 1467–1484 (2009)
Bhowmik, T.K., Bhattacharya, U., Parui, Swapan K.: Recognition of bangla handwritten characters using an MLP classifier based on stroke features. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 814–819. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30499-9_125
Bhattacharya, U., Shridhar, M., Parui, S., Sen, P., Chaudhuri, B.: Offline recognition of handwritten Bangla characters: an efficient two-stage approach. Pattern Anal. Appl. 15, 445–458 (2012). https://doi.org/10.1007/s10044-012-0278-6
Center for Microprocessor Application for Training Education and Research (CMATER). Computer Science and Engineering Department, Jadavpur University, Kolkata, India. https://code.google.com/archive/p/cmaterdb
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Basu, S., et al.: Handwritten Bangla alphabet recognition using an MLP based classifier. In: Proceedings of the Second National Conference on Computer Processing of Bangla, pp. 285–291 (2005)
Bhattacharya, U., Parui, S.K., Sridhar, M., Kimura, F.: Two-stage recognition of handwritten Bangla alphanumeric characters using neural classifiers. In: Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI), pp. 1357–1376 (2005)
Bhattacharya, U., Shridhar, M., Parui, S.: On recognition of handwritten Bangla characters, pp. 817–828. Comput. Vis., Graphics and Image Processing (2006)
Adnan, M., Rahman, F., Imrul, M., Nasib, A.L., Shabnam, S.: Handwritten Bangla character recognition using inception convolutional neural network. Int. J. Comput. Appl. 181, 48–59 (2018)
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Mondal, S., Mahfuz, N. (2020). Convolutional Neural Networks Based Bengali Handwritten Character Recognition. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_57
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