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Publication . Article . 2020

Automated bank cheque verification using image processing and deep learning methods

Prateek Agrawal; Deepak Chaudhary; Vishu Madaan; Anatoliy Zabrovskiy; Radu Prodan; Dragi Kimovski; Christian Timmerer;
Open Access  
Published: 06 Oct 2020 Journal: Multimedia Tools and Applications, volume 80, pages 5,319-5,350 (issn: 1380-7501, eissn: 1573-7721, Copyright policy )
Publisher: Springer Science and Business Media LLC
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%.
Subjects by Vocabulary

Microsoft Academic Graph classification: Deep learning Cashier's check Cheque truncation Artificial intelligence business.industry business Cheque Data mining computer.software_genre computer Computer science Image processing


Computer Networks and Communications, Hardware and Architecture, Media Technology, Software

Funded by
smART socIal media eCOsytstem in a blockchaiN Federated environment
  • Funder: European Commission (EC)
  • Project Code: 825134
  • Funding stream: H2020 | RIA
Validated by funder
dEcentralized repositories for traNsparent and efficienT vIrtual maChine opErations
  • Funder: European Commission (EC)
  • Project Code: 644179
  • Funding stream: H2020 | RIA
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Multimedia Tools and Applications
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Providers: UnpayWall