Handwritten Character Recognition by using Convolutional Deep Neural Network; Review

Year 2019, Volume: 5 Issue: 1, 1 - 5, 29.03.2019

Baki Koyuncu , Hakan Koyuncu

https://izlik.org/JA58BA43GW

Abstract

Handwritten character recognition is an important
domain of research with implementation in varied fields.  Past and recent works in this field focus on
diverse languages to utilize the character recognition in automated data-entry
applications. Deep Neural network studies recognize the individual characters
in the form images. The reliance of each recognition, which is provided by the
neural network as part of the ranking result, is one of the things used to
customize the implementation to the request of the client. Convolutional Deep neural
network model is reviewed to recognize the handwritten characters in this
study. This model, initially, learned a useful set of support by using core and
local receptive areas and then a densely connected network layers are employed for
the discernment task.  

Keywords

Handwritten Character Recognition , Deep Neural Network (DNN) , Deep Convolutional Neural Network (DCNN)

References

• R. Vaidya, D. Trivedi, S. Satra, M. Pimpale,“Handwritten Character Recognition Using DeepLearning”. Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 772-775, 2018
• G. S. Budhi and R. Adipranata, “Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods”, J.ICT Res. Appl., vol. 8, no. 3, pp. 195–212, 2015
• A. Rajavelu, M.T. Musavi, and M.V. Shirvaikar, “A neural network approach to character recognition”, Neural Netw., vol. 2, no. 5, pp. 387– 393, 1989.
• S. Mori, C. Y. Suen, and K. Yamamoto, “Historical review of OCR research and development,” Proc. IEEE, vol. 80, no. 7, pp. 1029 –1058, 1992
• J. Pradeep, E. Srinivasan and S. Himavathi. “Neural Network based Handwritten Character Recognition system without feature extraction“, International Conference on Computer, Communication and Electrical Technology ICCCET 2011
• K. Gurney, “An introduction to neural networks”, UCL Press, 1997
• Y. LeCun, Y. Bengio and G. Hinton, "Deep learning", Nature, Vol. 521, pp. 436-444, 2015
• Y. Liang, J. Wang, S. Zhou, Y. Gong, and N. Zheng, “Incorporating image priors with deep convolutional neural networks for image super resolution”, Neurocomputing, Vol. 194, pp. 340- 347, 2016
• R. Nijhawan, H. Sharma, H. Sahni, and A. Batra, “A deep learning hybrid CNN framework approach for vegetation cover mapping using deep features”, 13th International Conference on Signal Image Technology & Internet-Based Systems (SITIS), pp. 192-196, 2017
• K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biological Cybernetics, vol. 36, no. 4, pp. 193–202, 1980
• A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097–1105, 2012
• C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling”, IEEE Trans. Pattern Anal. Mach. Intel., Vol. 35, no. 8, pp. 1915–1929, 2013
• O. Vinyals, A. Toshev, S. Bengio, and D. Ethan, “Show and tell: A neural image caption generator”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164, 2015
• D. C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification”, Proceedings in 22nd International Joint Conference on Artificial Intelligence, Vol. 22, pp. 1237-1242, 2011
• E. Kussul and T. Baidyk, “Improved method of handwritten digit recognition tested on MNIST database”, Image Vis. Compute., vol. 22, no. 12, pp. 971–981, 2004
• W. Lu, Z. Li, B. Shi.” Handwritten Digits Recognition with Neural Networks and Fuzzy Logic”, IEEE International Conference on Neural Networks, Vol. 3, pp.1389-1392, 1995
• P. Banumathi, G. M. Nasira, “Handwritten Tamil Character Recognition using Artificial Neural Networks”, International Conference on Process Automation, Control and Computing, 2011
• B. V. S. Murthy,” Handwriting Recognition Using Supervised Neural Networks”, International Joint Conference on Neural Networks, 1999