Abstract
A large number of bank checks are processed manually every day, across the world. In a developing nation like India, cheques are significant instruments for achieving cashless transactions. Cheque processing is a tedious task that can be automated with advanced deep learning architectures. Cheque automation involves selecting the Regions Of Interest (ROI) and then analyzing the contents in the ROI. In this paper, we propose a novel approach to extract ROI (fields) on the cheque using a Convolutional Neural Network (CNN)-based object detection algorithms like YOLO. By virtue of employing a CNN-based model, our approach turns out to be scale, skew, and shift invariant. We achieved a mean average precision (mAP) score of 86.6% across all the fields on a publicly available database of cheques. On the extracted logo field from YOLO, we performed logo recognition using VGGnet as a feature extractor and achieved an accuracy of 99.01%.
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Vishnuvardhan, G., Ravi, V., Mallik, A.R. (2021). Field Extraction and Logo Recognition on Indian Bank Cheques Using Convolution Neural Networks. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_26
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