Automated bank cheque verification using image processing and deep learning methods

• Published in: Multimedia tools and applications Vol. 80; no. 4; pp. 5319 - 5350
• Main Authors: Agrawal, Prateek, Chaudhary, Deepak, Madaan, Vishu, Zabrovskiy, Anatoliy, Prodan, Radu, Kimovski, Dragi, Timmerer, Christian
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-02-2021; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Automation; Banking; Checks; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Feature extraction; Handwriting recognition; Image processing; Machine learning; Multimedia Information Systems; Optical character recognition; Special Purpose and Application-Based Systems; Support vector machines; Teaching methods; Verification; Image segmentation; Convolution neural network; Image feature extraction; Scale invariant feature transform; Cheque truncation system; Support vector machine; Bank cheque clearance
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09818-1

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%.