Manipuri Handwritten Character Recognition by Convolutional Neural Network

Handwritten character recognition is an essential field in pattern recognition. Its popularity is increasing with the potential to thrive in various applications such as banking, postal automation, form filling, etc. However, developing such a system is a challenging task with the diverse writing style of the same character, and present of visually similar characteristics. In this paper, a recognition system is proposed using a deep neural network. The performance of the network is investigated on a self-collected handwritten dataset of Manipuri script contributed by 90 different people of varying age and education. A total of 4900 sample images is considered for the experiment and recorded a recognition rate of 98.86%.

[1]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[2]  N. P. Thulasi Kishna,et al.  Intelligent tool for Malayalam cursive handwritten character recognition using artificial neural network and Hidden Markov Model , 2017, 2017 International Conference on Inventive Computing and Informatics (ICICI).

[3]  Lambert Schomaker,et al.  A Comparison of Feature and Pixel-Based Methods for Recognizing Handwritten Bangla Digits , 2013, 2013 12th International Conference on Document Analysis and Recognition.

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

[5]  Yoshua Bengio,et al.  Drawing and Recognizing Chinese Characters with Recurrent Neural Network , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Salvador España Boquera,et al.  Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Md Saiful Islam,et al.  Bengali handwritten character recognition using deep convolutional neural network , 2017, 2017 20th International Conference of Computer and Information Technology (ICCIT).

[8]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[9]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[10]  Feng Tian,et al.  Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Seema Bawa,et al.  Recognition of Handwritten Character of Manipuri Script , 2010, J. Comput..

[12]  Muhammad Abul Hasan,et al.  Isolated Bangla handwritten character recognition with convolutional neural network , 2017, 2017 20th International Conference of Computer and Information Technology (ICCIT).

[13]  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.

[14]  Subhankar Ghosh,et al.  An OCR system for the Meetei Mayek script , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[15]  Nikita Singh.  An Efficient Approach for Handwritten Devanagari Character Recognition based on Artificial Neural Network , 2018, 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN).

[16]  Romesh Laishram,et al.  A neural network based handwritten Meitei Mayek alphabet optical character recognition system , 2014, 2014 IEEE International Conference on Computational Intelligence and Computing Research.

[17]  Ariyanto,et al.  Neural Networks for Lampung Characters Handwritten Recognition , 2016, 2016 International Conference on Computer and Communication Engineering (ICCCE).

[18]  Prakash Choudhary,et al.  Recognition of Handwritten Meitei Mayek and English Alphabets Using Combination of Spatial Features , 2018, ISDA.

[19]  Samad Roohi,et al.  Persian handwritten character recognition using convolutional neural network , 2017, 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP).

[20]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[21]  Monji Kherallah,et al.  Arabic handwritten characters recognition using Deep Belief Neural Networks , 2015, 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15).

[22]  Xiaobo Zhang,et al.  A Novel Method for Recognition of Persian Alphabet by Using Fuzzy Neural Network , 2018, IEEE Access.

[23]  Prakash Choudhary,et al.  Recognition of Handwritten Meitei Mayek Script Based on Texture Feature , 2018 .