Abstract: Automated cheque processing has become increasingly important in the banking and financial sectors. With the growing volume of cheque transactions, manual processing has become inefficient, error-prone, and time-consuming. Automated systems have emerged as a solution to streamline the cheque processing workflow, improve accuracy, and reduce operational costs. In today's financial landscape, the automation of cheque processing has emerged as a critical need for enhancing operational efficiency and accuracy in banking institutions. The transition from manual to automated cheque processing systems has become imperative to meet the demands of a rapidly evolving financial ecosystem.
The automated cheque processing system employs advanced image recognition and machine learning algorithms to capture, extract, and validate crucial information from the scanned checks. Through optical character recognition (OCR), signature verification, and fraud detection techniques, this system enhances the accuracy and security of cheque processing while minimizing human intervention.
Neural network-driven deep learning algorithms are used to automatically extract pertinent data from checks, including account numbers, amounts, and payee information. These algorithms have overcome the difficulties presented by different handwriting styles by undergoing intensive training on big datasets, enabling them to interpret handwritten text and signatures with accuracy.
In conclusion, deep learning based automatic check processing provide a revolutionary way to modernise financial transactions while enhancing dependability, and efficiency.
|
DOI:
10.17148/IJARCCE.2024.13570
