Abstract
Indian subcontinent is a birthplace of multilingual people where documents such as job application form, passport, number plate identification and so forth are written in different languages. These languages may be in the form of different Indic digits in a single page. So, building a generic recognizer that is capable of recognizing handwritten Indic digits written by diverse writers is needed. Also, numerous works have been done on non-Indic numerals particularly, in case of Roman, but, in case of Indic digits, the research is limited. Moreover, most of the research focuses only on MNIST datasets or with only single datasets, either because of time restraints or because the model is tailored to a specific task. In this work, a hybrid model is developed to recognize all available Indic handwritten digit images using the existing benchmark datasets. The proposed method bridges the automatically learnt features of capsule network with handcrafted bag of feature (BoF) extraction method. Along the way, we analyze (1) the successes and (2) explore whether this method will perform well on more difficult conditions, i.e., noise, color, affine transformations, intra-class variation and natural scenes. Experimental results confirm that the developed method gives better accuracy in comparison with capsule network.
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Reduanul Haque, M., Hafiz, R., Zahidul Islam, M., Khatun, A., Akter, M., Shorif Uddin, M. (2021). Handwritten Indic Digit Recognition Using Deep Hybrid Capsule Network. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0586-4_43
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