Capsule Networks for Character Recognition in Low Resource Languages
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Most of the existing techniques in handwritten character recognition are not well-utilized for low resource languages, due to the lack of labelled data and the need for large datasets for image classification using deep neural networks. In contrast to recent advancement in deep learning-based image classification, human cognition could quickly identify and differentiate characters without much training. As a solution to character recognition problem in low resource languages, this chapter proposes a model that replicates the human cognition ability to learn with small datasets. The proposed solution is a Siamese neural network which bestows capsules and convolutional units to get a thorough understanding of the image. The presented model takes two images as inputs, process, and extract features through the capsule network and outputs the probability of being similar. This study attests that the capsule-based Siamese network could learn abstract knowledge about different characters which could be extended to unforeseen characters. The proposed model is trained on Omniglot dataset and achieved up to 94% accuracy for previously unseen alphabets. Further, the module is tested on Sinhala language alphabet and MNIST dataset that stands for Modified National Institute of Standards and Technology database, which are new to the trained model.
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