A Two-stage CNN Based Computer Vision Framework for Automated Validation of Indian Bank Cheques

Abstract

Automated bank cheque processing is still considered a challenging task for computer vision researchers. This article proposes a two-stage deep learning-based end-to-end computer vision framework to validate an Indian bank cheque with respect to a few mistakes commonly found in manually entered handwritten fields. The proposed framework primarily works in two stages involving two separate Mask RCNN models to detect two common mistakes due to the absence of any key handwritten field or the presence of any overwritten/strike-through handwritten character in the bank cheque image. The first stage of the Mask RCNN model aims to segment all the key handwritten fields in a bank cheque image, leading to the detection of any missing handwritten field. The second stage, the Mask RCNN model, attempts to detect the presence of any overwritten/strike-through handwritten character in a bank cheque image that may lead to the invalidation of the cheque. Due to the unavailability of any standard dataset for validation purposes, a bank cheque image data repository has been prepared exclusively for developing the proposed framework. Extensive experimentation with the prepared dataset reveals that the proposed framework can outperform some of the popular frameworks by achieving a promising accuracy (98%) in terms of reporting validation errors owing to the aforementioned mistakes in the bank cheque.