Modified Convolutional Neural Network of Tamil Character Recognition

The handwritten Tamil character recognition in offline mode is challenging tasks as there are virtually different people who have different styles of writing the same characters. Deep convolution neural networks are playing a virtual role nowadays in recognizing handwritten character by automatically learning discriminative features from high dimensionality of input data. This work presents a modified convolution neural network \(\left( \text {M-CNN} \right) \) architecture to achieve a faster convergence rate and also to get the highest recognition accuracy. The M-CNN on different aspects along with layers design, activation function, loss function and optimization is discussed. Systematic experiments on isolated handwritten Tamil character dataset collected from various schools by ourselves. For these collected datasets, the proposed system recognized the characters with 97.07%.

[1]  Sumit Srivastava,et al.  Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods , 2018, J. Imaging.

[2]  Ankur Singh Bist,et al.  A Wide Scale Survey on Handwritten Character Recognition using Machine Learning , 2019, International Journal of Computer Sciences and Engineering.

[3]  Zhiyuan Li,et al.  Building efficient CNN architecture for offline handwritten Chinese character recognition , 2018, International Journal on Document Analysis and Recognition (IJDAR).

[4]  C. Sureshkumar.  Handwritten Tamil Character Recognition and Conversion using Neural Network , 2010 .

[5]  Fumitaka Kimura,et al.  Tamil Handwritten City Name Database Development and Recognition for Postal Automation , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[6]  Hazem M. El-Bakry,et al.  Arabic Handwritten Characters Recognition Using Convolutional Neural Network , 2017 .

[7]  Mahantapas Kundu,et al.  Handwritten isolated Bangla compound character recognition: A new benchmark using a novel deep learning approach , 2017, Pattern Recognit. Lett..

[8]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[9]  C. Srimathi,et al.  Benchmarking on offline Handwritten Tamil Character Recognition using convolutional neural networks , 2019, J. King Saud Univ. Comput. Inf. Sci..

[10]  Parul Sahare,et al.  Multilingual Character Segmentation and Recognition Schemes for Indian Document Images , 2018, IEEE Access.

[11]  Ching Y. Suen,et al.  Automatic recognition of serial numbers in bank notes , 2014, Pattern Recognit..

[12]  Wenju Liu,et al.  Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data , 2018, Pattern Recognit. Lett..

[13]  Lianwen Jin,et al.  DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition , 2015, Pattern Recognit..

[14]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[15]  Raymond W. Ptucha,et al.  Intelligent character recognition using fully convolutional neural networks , 2019, Pattern Recognit..

[16]  Mohamed Batouche,et al.  Investigation on deep learning for off-line handwritten Arabic character recognition , 2017, Cognitive Systems Research.

[17]  Yoshua Bengio,et al.  Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark , 2016, Pattern Recognit..

[18]  Levan Shugliashvili,et al.  On Georgian Handwritten Character Recognition , 2018 .