Handwritten MODI Character Recognition Using Transfer Learning with Discriminant Feature Analysis

References

• U. Pal, and B. B. Chaudhuri, “Indian script character recognition: a survey,” Pattern Recognit., Vol. 37, no. 9, pp. 1887–1899, Sep. 2004.
   Web of Science ®Google Scholar
• U. Pal, B. B. Chaudhuri, and A. Belaïd, “A complete system for bangla handwritten numeral recognition,” IETE. J. Res., Vol. 52, no. 1, pp. 27–34, Jan. 2006.
   Web of Science ®Google Scholar
• S. P. Ramteke, A. A. Gurjar, and D. S. Deshmukh, “A novel weighted SVM classifier based on SCA for handwritten marathi character recognition,” IETE. J. Res., 1–13, Jun. 2019. DOI: 10.1080/03772063.2019.1623093.
   Web of Science ®Google Scholar
• M. Kumar, M. K. Jindal, R. K. Sharma, and S. R. Jin-dal, “Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study,” Artif. Intell. Rev., Vol. 53, no. 3, pp. 2075–2097, 2020.
   Web of Science ®Google Scholar
• S. Narang, M. K. Jindal, and M. Kumar, “Devanagari ancient documents recognition using statistical feature extraction techniques,” SāDhanā, Vol. 44, no. 6, pp. 1–8, 2019.
   Web of Science ®Google Scholar
• M. Kumar, S. R. Jindal, M. K. Jindal, and G. S. Lehal, “Improved recognition results of medieval handwritten guru mukhi manuscripts using boosting and bagging methodologies,” Neural Process. Lett., Vol. 50, no. 1, pp. 43–56, 2019.
   Web of Science ®Google Scholar
• P. Sohoni, “Marathi of a single type: The demise of the modi script,” Mod. Asian. Stud., Vol. 51, no. 3, pp. 662–685, May 2017.
   Web of Science ®Google Scholar
• J. Ranjan, and S. Bhattacharyya. “Character recognition from handwritten image using convolutional neural networks,” in Recent Trends in Signal and Image Processing, Springer, Singapore, 2019, pp. 23–30.
   Google Scholar
• H. Dongmei, Q. Liu, and W. Fan, “A new image classification method using CNN transfer learning and web data augmentation,” Expert. Syst. Appl., Vol. 95, pp. 43–56, Apr. 2018.
   Web of Science ®Google Scholar
• S. L. Chandure, and V. Inamdar, “Performance analysis of handwritten Devnagari and MODI character recognition system,” in Int. Conf. on Computing, Analytics and Security Trends (CAST), IEEE, 513–516, Dec. 2016. DOI: 10.1109/CAST.2016.7915022.
   Google Scholar
• M. Kumar, M. K. Jindal, R. K. Sharma, and S. R. Jindal, “Character and numeral recognition for non-indic and in-dic scripts: a survey,” Artif. Intell. Rev., Vol. 52, no. 4, pp. 2235–2261, 2019.
   Web of Science ®Google Scholar
• P. Giuseppe, and D. Impedovo, “Adaptive membership functions for handwritten character recognition by voronoi-based image zoning,” IEEE Trans. on Image Processing, Vol. 21, no. 9, pp. 3827–3837, May 2012.
   PubMed Web of Science ®Google Scholar
• S. Cun-Zhao, S. Gao, M.-T. Liu, C.-Z. Qi, C.-H. Wang, and B.-H. Xiao, “Stroke detector and structure based models for character recognition: A comparative study,” IEEE Trans. on Image Processing, Vol. 24, no. 12, pp. 4952–4964, Aug. 2015.
   PubMed Web of Science ®Google Scholar
• Y. Fang, X.-D. Tian, X. Zhang, and X.-B. Jia. “An Improved method for similar handwritten Chinese character recognition,” in IEEE Intelligent Information Technology and Security Informatics (IITSI), Third International Symposium, Apr. 2010, pp. 419–422.
   Google Scholar
• S. Arora, D. Bhattacharjee, M. Nasipuri, D. K. Basu, and M. Kundu. “Combining multiple feature extraction techniques for handwritten Devnagari character recognition,” in IEEE Region 10 and the Third Int. Conf. on Industrial and Information Systems, Dec. 2008, pp. 1–6.
   Google Scholar
• S. R. NaraFFng, M. K. Jindal, and M. Kumar, “Devanagari ancient character recognition using DCT features with adaptive boosting and bootstrap aggregating,” Soft. comput., Vol. 23, no. 24, pp. 13603–13614, 2019.
   Web of Science ®Google Scholar
• S. R. Narang, M. K. Jindal, S. Ahuja, and M. Kumar, “On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features,” Soft. comput., Vol. 24, no. 22, pp. 17279–17289, 2020.
   Web of Science ®Google Scholar
• M. Kumar, M. K. Jindal, R. K. Sharma, and S. R. Jin- dal, “Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study,” Artif. Intell. Rev., Vol. 53, no. 3, pp. 2075–2097, 2020.
   Web of Science ®Google Scholar
• M. Kumar, M. K. Jindal, and R. K. Sharma, “Performance comparison of several feature selection techniques for offline handwritten character recognition,” 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), 1–6, 2018. DOI: 10.1109/RICE.2018.8509076.
   Google Scholar
• M. Kumar, and S. R. Jindal, “A study on recognition of pre-segmented handwritten multi-lingual characters,” Archives of Computational Methods in Engineering, Vol. 27, no. 2, pp. 577–589, 2020.
   Web of Science ®Google Scholar
• M. Kumar, M. K. Jindal, and R. K. Sharma, ““Offline hand-written Gurmukhi character recognition: analytical study of different transformations,” Proc. Natl. Acad. Sci., Vol. 87, no. 1, pp. 137–143, 2017.
   Web of Science ®Google Scholar
• S. N. Reddy, and P. S. Deshpande, “Review of feature extraction techniques for character recognition,” IETE. J. Res., Vol. 64, no. 2, pp. 280–295, Mar. 2018.
   Web of Science ®Google Scholar
• B. R. Kavitha, and C. Srimathi, “Benchmarking on offline handwritten Tamil character recognition using conVolutional neural networks,” Journal of King Saud University-Computer and Information Sciences (Jun. 2019). DOI: 10.1016/j.jksuci.2019.06.004.
   Google Scholar
• L. Dazhen, F. Lin, Y. Lv, F. Cai, and D. Cao, “Chinese character captcha recognition and performance estimation via deep neural network,” Neurocomputing, Vol. 288, pp. 11–19, May 2018.
   Web of Science ®Google Scholar
• G. Ji, W. Wang, and K. Lu, “A new perspective: recognizing online handwritten Chinese characters via 1-dimensional CNN,” Inf. Sci. (Ny), Vol. 478, pp. 375–390, Apr. 2019.
   Web of Science ®Google Scholar
• B. Chaouki, A. Kerboua, and M. Batouche, “Investigation on deep learning for off-line handwritten arabic character recognition,” Cogn. Syst. Res., Vol. Vol. 50, pp. 180–195, Aug. 2018.
   Web of Science ®Google Scholar
• B. Dessai, and A. Patil. “A deep learning approach for optical character recognition of handwritten Devanagari script,” in 2nd Int. Conf. on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Vol. 1, pp. 1160–1165. IEEE, Jul. 2019.
   Google Scholar
• P. S. Jialin, and Q. Yang, “A survey on transfer learning,” IEEE Trans. on Knowledge and Data Engineering, Vol. 22, no. 10, pp. 1345–1359, Oct. 2009.
   Web of Science ®Google Scholar
• Z. Yongchun, F. Zhuang, J. Yang, X. Yang, and Q. He. “Adaptively transfer category-classifier for handwritten Chinese character recognition,” in Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Springer, Cham, Apr. 2019, pp. 110–122.
   Google Scholar
• T. Yejun, L. Peng, Q. Xu, Y. Wang, and A. Furuhata. “CNN based transfer learning for historical Chinese character recognition,” in 12th IAPR Workshop on Document Analysis Systems (DAS), IEEE, Apr. 2016, pp.25–29.
   Google Scholar
• T. Yejun, B. Wu, L. Peng, and C. Liu. “Semi-supervised transfer learning for convolutional neural network based Chinese character recognition,” in 14th IAPR Int. Conf. on Document Analysis and Recognition (ICDAR), IEEE, Nov. 2017, pp. 441–447.
   Google Scholar
• X. Yong, X. zhao, F. J. Wu, X. Li, and D. Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation,” IEEE Trans. on Image Processing, Vol. 25, no. 2, pp. 850–863, Dec. 2015.
   PubMed Web of Science ®Google Scholar
• P. K. Sonawane, and S. Shelke. “Handwritten devanagari character classification using deep learning,” in Int. Conf. on Information, Communication, Engineering and Technology, ICICET, Aug. 2018, pp. 1–4.
   Google Scholar
• D. Peng, Y. Wang, C. Liu, and Z. Chen, “TL-NER: A transfer learning model for Chinese named entity recognition,” Inf. Syst. Front., Vol. 4, pp. 1–4, Jun. 2019.
   Google Scholar
• U. Pal, T. Wakabayashi, and F. Kimura. “Comparative study of Devnagari handwritten character recognition using different feature and classifiers,” in Proceedings of the 10th International Conference on Document Analysis and Recognition, 2009, pp. 1111–1115.
   Google Scholar
• K. Alex, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances In Neural Information Processing Systems, Vol. 25, pp. 1097–1105, 2012.
   Google Scholar
• J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei. “Imagenet: a large-scale hierarchical image database,” in IEEE Conf. on Computer Vision and Pattern Recognition, Jun. 2009, pp. 248–255.
   Google Scholar
• F. Johannes, “Round robin classification,” J. Mach. Learn. Res., Vol. 2, pp. 721–747, Mar. 2002.
   Web of Science ®Google Scholar
• W. Zhou, A. C. Bovik, H. R. Sheikh, and E. P. Simoncell, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Processing, Vol. 13, no. 4, pp. 600–612, Apr. 2004.
   PubMed Web of Science ®Google Scholar
• S. A. Kulkarni, P. L. Borde, R. R. Manza, and P. L. Yannawar. “Impact of zoning on Zernike moments for handwritten MODI character recognition,” In Int. Conf. on Computer, Communication and Control (IC4), IEEE, Spt. 2015, pp. 1–6.
   Google Scholar
• M. Jangid, and S. Srivastava, “Handwritten Devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods,” Journal of Imaging, Vol. Vol. 4, no. 2, pp. 41, Feb. 2018.
   Web of Science ®Google Scholar
• B. Chakraborty, B. Shaw, J. Aich, U. Bhattacharya, and S. K. Parui. “Does deeper network lead to better accuracy: A case study on handwritten Devanagari characters,” in 13th IAPR International Workshop on Document Analysis Systems, DAS, Apr. 2018, pp. 411–416.
   Google Scholar