Handwritten Character Recognition by using Convolutional Deep Neural Network; Review

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.

[1]  Ching Y. Suen,et al.  Historical review of OCR research and development , 1992, Proc. IEEE.

[2]  Rohan Vaidya,et al.  Handwritten Character Recognition Using Deep-Learning , 2018, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).

[3]  Anjusha Pimpalshende,et al.  A Neural Network Approach to Character Recognition , 2015 .

[4]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[6]  Nanning Zheng,et al.  Incorporating image priors with deep convolutional neural networks for image super-resolution , 2016, Neurocomputing.

[7]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[8]  S. Himavathi,et al.  Neural network based handwritten character recognition system without feature extraction , 2011, 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET).

[9]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Bingxue Shi,et al.  Handwritten digits recognition with neural networks and fuzzy logic , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Gregorius Satia Budhi,et al.  Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods , 2014 .

[13]  B. V. S. Murthy Handwriting recognition using supervised neural networks , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[14]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[15]  Himanshu Sharma,et al.  A Deep Learning Hybrid CNN Framework Approach for Vegetation Cover Mapping Using Deep Features , 2017, 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).