Abstract
Legal amount detection is a decade old conundrum hindering the efficiency of automatic cheque detection systems. Ever since the advent of legal amount detection as a use-case in the computer vision ecosystem, it has been hampered by the deficiency of effective machine learning models to detect the language-specific legal amount on bank cheques. Currently, convolutional neural networks are the most widely used deep learning algorithms for image classification. Yet the majority of deep learning architectures fail to capture information like shape, orientation, pose of the images due to the use of max pooling. This paper proposes a novel way to extract, process and segment legal amounts into words from Indian bank cheques written in English and recognize them. The paper uses capsule networks to recognize legal amounts from the bank cheques, which enables the shape, pose and orientation detection of legal amounts by using dynamic routing and routing by agreement techniques for communication between capsules and thus improves the recognition accuracy.
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Mistry, N., Darisi, M., Singh, R., Shah, M., Malshikhare, A. (2020). Legal Amount Recognition in Bank Cheques Using Capsule Networks. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_3
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DOI: https://doi.org/10.1007/978-981-15-6318-8_3
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