Automated bank cheque verification using image processing and deep learning methods
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Automated bank cheque verification using image processing and deep learning methods Prateek Agrawal1,2 · Deepak Chaudhary2 · Vishu Madaan2 · Anatoliy Zabrovskiy1,3 · Radu Prodan1 · Dragi Kimovski1 · Christian Timmerer1,4 Received: 20 October 2019 / Revised: 29 July 2020 / Accepted: 2 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
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%. Keywords Cheque truncation system · Image segmentation · Bank cheque clearance · Image feature extraction · Convolution neural network · Support vector machine · Scale invariant feature transform
Vishu Madaan
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Extended author information available on the last page of the article.
Multimedia Tools and Applications
1 Introduction Cheques have an intriguing past, as evidently, it is still a mystery as to when exactly the concept of cheque has emerged. Speculations were made that the idea in itself was entertained during the Roman empire, however the very idea could not catch on. Thus, its usage came in the business hubs in the 1500s in regions near Holland and Amsterdam because of their richness in business opportunities during that time. Therefore, with its gradual acceptance in the commerce and enhanced usability it was slowly accepted as a means of monetary transaction. The word “cheque” was evidently originated in the 1700s as a common practice for monetary transact
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