Automated Bank Cheque Processing System

Abstract

Abstract: While digital technology has advanced rapidly in recent years, many financial institutions such as banks still rely on conventional methods to process bank cheques manually. This process is time-consuming and can take several days for the transfer of funds to be completed as it involves verification by intermediaries. As a result, costs can be high. This proposed system aims to streamline the cheque processing process, reduce costs, and improve efficiency. This system will facilitate the process and lead to reduction in time and costs. When it comes to the clearance of bank cheques and monetary transactions, it should not only be dependable and robust, but it should also save time, which is a crucial element in nations with a large population. By automating the entire cheque processing workflow, banks can process cheques faster and more accurately, which improves customer satisfaction and reduces costs associated with manual processing. In this paper, we propose an automated system which extracts relevant details of a bank cheque like Payee Name, Amount in words and number, date, bank name, cheque number, IFSC code, using machine learning algorithms, Optical Character Recognition (OCR) to extract relevant data from the cheque image, deep learning and Convolutional Neural Networks (CNN) to verify that the extracted data matches the information on file, image segmentation to separate different components of the cheque, and image feature extraction to identify patterns and features in the cheque image that can help detect signs of fraud or forgery. It also uses feature extraction to verify the signature on the cheque with the existing signature stored in the database. Additionally, the combination of these methods has improved to reduce errors, making automated bank cheque processing utilizing machine learning a potent tool for banks wishing to automate their check processing procedures.