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Automated bank cheque verification using image processing and deep learning methods

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Abstract

Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.

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Acknowledgements

This work has been partly supported by the European Union Horizon 2020 Research and Innovation Programme under the ARTICONF Project with grant agreement number 644179.

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Authors and Affiliations

  1. University of Klagenfurt, Klagenfurt, Austria

    Prateek Agrawal, Anatoliy Zabrovskiy, Radu Prodan, Dragi Kimovski & Christian Timmerer

  2. Lovely Professional University, Phagwara, India

    Prateek Agrawal, Deepak Chaudhary & Vishu Madaan

  3. Petrozavodsk State University, Petrozavodsk, Russia

    Anatoliy Zabrovskiy

  4. Bitmovin Inc., San Francisco, CA, USA

    Christian Timmerer

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  1. Prateek Agrawal
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Agrawal, P., Chaudhary, D., Madaan, V. et al. Automated bank cheque verification using image processing and deep learning methods. Multimed Tools Appl 80, 5319–5350 (2021). https://doi.org/10.1007/s11042-020-09818-1

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