Abstract
A new technique for classifying all 56 different characters of the Manipuri Meetei-Mayek (MMM) is proposed herein. The characters are grouped under five categories, which are Eeyek Eepee (original alphabets), Lom Eeyek (additional letters), Cheising Eeyek (digits), Lonsum Eeyek (letters with short endings), and Cheitap Eeyek (vowel signs. Two related works proposed by previous researchers are studied for understanding the benefits claimed by the proposed deep learning approach in handwritten Manipuri Meetei-Mayek. (1) Histogram of Oriented (HOG) with SVM classifier is implemented for thoroughly understanding how HOG features can influence accuracy. (2) The handwritten samples are trained using simple Convolutional Neural Network (CNN) and compared with the proposed CNN-based architecture. Significant progress has been made in the field of Optical Character Recognition (OCR) for well-known Indian languages as well as globally popular languages. Our work is novel in the sense that there is no record of work available to date that is able to classify all 56 classes of the MMM. It will also serve as a pre-cursor for developing end-to-end OCR software for translating old manuscripts, newspaper archives, books, and so on.
- Wangkhemcha Chingtamlen. 2007. A Short History of Kangleipak (Manipur) Part-II, Kangleipak Historical 8 Cultural Research Centre. Sagolband Thangjam Leirak, Imphal, India.Google Scholar
- Ng. Kangjia Mangang. 2003. Revival of a Closed Account, a Brief History of Kanglei Script and the Birth of Phoon (Zero) in the World of Arithmetic and Astrology. Sanamahi Laining Amasung Punshiron Khupham (Salai Punshipham), Lamshang, Imphal.Google Scholar
- T. C. Hodson. 1908. The Meitheis. Low Price Publications, Delhi.Google Scholar
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521 (2015), 436--444.Google Scholar
Cross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS). 1097--1105. Google Scholar
Digital Library
- Max Jadenberg, Karen Simonyab, Andrea Vedaldi, and Andrew Zisserman. 2016. Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis. 116, 1 (2016), 1--20. Google Scholar
Digital Library
- Chen-Yu Lee and Simon Osindero. 2016. Recursive recurrent nets with attention modeling for OCR in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 2231--2239.Google Scholar
Cross Ref
- T. Bluche, H. Ney, and C. Kermorvant. 2014. A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition. In Statistical Language and Speech Processing, L. Besacier, A. H. Dediu, C. Martín-Vide (Eds.), Lecture Notes in Computer Science, Vol. 8791, 1--12.Google Scholar
- D. K. Sahu and M. Sukhwani. 2015. Sequence to sequence learning for optical character recognition. CoRR, vol. abs/1511.04176 (2015) 1--9.Google Scholar
- Theodore Bluche, Jerome Louradour, Ronaldo Messina. 2016. Scan, attend and read: End-to-end handwritten paragraph recognition with MDLSTM attention. CoRR, vol. abs/1604.03286 (2016).Google Scholar
- Pan He, Weilin Huang, Yu Qiao, Chen Change Loy, and Xiaoou Tang. 2016. Reading scene text in deep convolutional sequences. In Proceedings of the Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI’16). 3501--3508. Google Scholar
Digital Library
- Tao Wang, David J. Wu, Adam Coates, and Andrew Y. Ng. 2013. End-to-end text recognition with convolutional neural networks. Proceedings of the 21st International Conference on Pattern Recognition.Google Scholar
- Thomas M. Breuel, Adanan Ul-Hasan, Mayce Al Azawi, and Faisal Shafait. 2013. High-performance ocr for printed english and fraktur using LSTM networks. Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR’13). 683--687. Google Scholar
Digital Library
- Anita Rani, Rajneesh Rani, and Renu Dhir. 2012. Combination of different feature sets and SVM classifier for handwritten gurumukhi numeral recognition. Int. J. Comput. Appl. 47, 18 (Jun. 2012), 28--33.Google Scholar
- Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, L. Malik, M. Kundu, and D. K. Basu. 2010. performance comparison of SVM and ANN for handwritten devanagri character recognition. Int. J. Comput. Sci. Issues 7, 3 (May 2010), 76--83.Google Scholar
- R. M. K. Sinha. 2009. A journey from Indian scripts processing to Indian language processing. IEEE Ann. Hist. Comput. 31, 1 (2009), 8--31. Google Scholar
Digital Library
- U. Pal, T. Wakabayashi, and F. Kimura. 2007. Handwritten Bangla compound character recognition using gradient feature. Proceedings of the 10th International Conference on Information Technology. 208--213. Google Scholar
Digital Library
- N. Sharma, U. Pal, F. Kimura, and S. Pal. 2006. Recognition of offline handwritten Devnagri characters using quadratic classifier. In Proceedings of the Indian Conference of Computer Vision Graphics and Image Processing. 808--816. Google Scholar
Digital Library
- Subhadip Basu, Nibaran Das, Ram Sarkar, Mahantapas Kundu, Mita Nasipuri, and Dipak Kumar Basu. 2005. Handwritten Bangla alphabet recognition using MLP based classifier. In Proceedings of the 2nd National Conference on Computer Processing of Bangla. 285--291. Dhaka.Google Scholar
- R. Plamondon and S. Srihari. 2000. Online and offline handwriting recognition: A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1 (2000), 63--84. Google Scholar
Digital Library
- Maring Kansham Angphun and Renu Dhir. 2014. Recognition of chesing Iyek/Eeyek-Manipuri digits using support vector machines. Int. J. Comput. Sci. Inf. Technol. 1, 2 (Apr. 2014), 1--6.Google Scholar
- Romesh Laishram, Pheiroijam Bebison Singh, Thokchom Suka Deba Singh, Sapam Anilkumar, and Angom Umakanta Singh. 2014. A neural network based handwritten Meetei Mayek alphabet optical character recognition system. In Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research.Google Scholar
- Chandan Jyoti Kumar and Sanjib Kumar Kalita. 2013. Recognition of handwritten numerals of Manipuri script. Int. J. Comput. Appl. 84, 17 (Dec. 2013), 1--5.Google Scholar
- Romesh Laishram, Angom Umakanta Singh, N. Chandrakumar Singh, A. Suresh Singh, and H. James. 2012. Simulation and modelling of handwritten meitei mayek digits using neural network approach. In Proceedings of the International Conference on Advances in Electronics, Electrical, Electrical and Computer Science Engineering (EEC’12). 355--358.Google Scholar
- Tangkeshwar Thokchom, P. K. Bansal, Renu Vig, and Seema Bawa. 2010. Recognition of handwritten character of Manipuri script. J. Comput. 5, 10 (Oct. 2010), 1570--1574.Google Scholar
Cross Ref
- Renato Kresch and David Malah. 1998. Skeleton-based morphological coding of binary images. IEEE Trans. Image Process. 7, 10 (Oct. 1998), 1387--1399. Google Scholar
Digital Library
- N. Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--8. Google Scholar
Digital Library
- N. Christianini and J. C. Shawe-Taylor. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK. Google Scholar
Digital Library
- S. Escalera, O. Pujol, and P. Radeva. 2009. Separability of ternary codes for sparse designs of error-correcting output codes. Pattern Recogn. Lett. 30, 3 (2009), 285--297. Google Scholar
Digital Library
- S. Escalera, O. Pujol, and P. Radeva. 2010. On the decoding process in ternary error-correcting output codes. IEEE Trans. Pattern Anal. Mach. Intell. 32, 7 (2010), 120--134. Google Scholar
Digital Library
- Chandan Jyoti Kumar and Sanjib Kumar Kalita. 2016. Geometrical, profile and HOG feature based recognition of Meetei Mayek characters. In Proceedings of the International Conference on Computing for Sustainable Global Development. 2841--2845.Google Scholar
Index Terms
Handwritten Manipuri Meetei-Mayek Classification Using Convolutional Neural Network
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