ABSTRACT: Fraudulent cheques are commonly distinguished using manual identification. Manual identification, unquestionably, is the least successful activity to battle against cheque frauds. This requires staff's ability to distinguish fake cheques dependent on the security highlights and other visual attributes. Furthermore, if the paper cheque is damaged, OCR will not be able to detect the cheque. Hence, its need to be cleared manually by a person. Then the automation process will not be successful. Moreover, current CITS based paper cheque clearance requires at least one day to clear a cheque which could extend up to three working days. Additionally, the user needs to go to the bank to deposit cheque with consuming both time and cost. Proposed system will be implemented as a python web application using Django framework. To verify the authenticity of the cheque, account holder’s signature on the cheque will be analyzed using deep learning techniques. Account holders’ signatures are collected, and system builds a deep learning model. Model is trained using account holder’s signature dataset by extracting the features from every signature image and labeling it. System uses CNN for training and classification.
KEYWORDS: Check truncation system, online banking, remote check deposit, digital check forgery, forgery detection, image forensics, expert system, JPEG artifacts.
Cite:
Anvitha Jain, Niha Kauser, Shravya KS, Sinchana Venugopal, Mr.Narendra UP,"Automated Bank Cheque Verification System", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133129.
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DOI:
10.17148/IJARCCE.2024.133129
