Abstract
Optical Character Recognition (OCR) is at the forefront of numerous applications such as digitalization of legal and legacy documents, automatic form processing, writer identification in forensic intelligence. Most of these applications seldom have sufficient training samples in order to achieve an accuracy worthy of real-time deployments. Inspired by the demonstrated performance of Siamese Neural Networks (SNN) in various fields such as Computer vision, Natural Language Processing, Signal processing etc., in this paper, we explore the application of SNN for Tamil Handwritten character recognition. The Siamese-CNN learning is implemented using cross-entropy loss and subsequently used to validate the few-shot learning. It achieved an optimal accuracy of 83.39% for n-way-40-shot learning. Rigorous experiments were conducted all through and the results are indicative of a promising new direction for the development of efficient Indic OCR models using Siamese networks.
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Shaffi, N., Hajamohideen, F. (2021). Few-Shot Learning for Tamil Handwritten Character Recognition Using Deep Siamese Convolutional Neural Network. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_16
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DOI: https://doi.org/10.1007/978-3-030-82269-9_16
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