Automatic imagery Bank Cheque data extraction based on machine learning approaches: a comprehensive survey

Bank Cheques are used mainly for financial transactions due to which they are processed in enormous amounts on daily basis around the globe. Often, Cheque execution time and expenses can be saved if the whole method of recognition and verification of the Cheque becomes automatic. Automatic bank Cheque processing system is an emerging research field in the area of computer vision, image processing, pattern recognition, machine learning, and deep learning. The article emphasizes the stages of the proceedings of image acquisition, pre-processing, and extraction and recognition in the automatic bank Cheque processing system. This paper describes the various steps involved in the system of automatic data extraction. It further classifies and examines existing challenges in different stages of automated processing of bank Cheques. An attempt is made in this paper to present state-of-the-art techniques for the automatic processing of bank Cheque images. The categories and sub-categories of various fields related to bank Cheque images are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. Moreover, it also contains some information about the products available in the market for automatic Cheque processing. This review provides a fundamental comparison and analysis of the remaining problems in the field. It is found that multilayer feed-forward neural network gave an accuracy of 97.31% for payee’s name recognition systems; HMM-MLP gave an accuracy of 95.5% for date recognition system. In the courtesy and legal amount system, DNN gave an accuracy of 98.5% for digit recognition, MLP gave an accuracy of 93.2% for courtesy amount, MQDF gave an accuracy of 97.04% for the legal amount. Further, the SVM classifier gave an accuracy of 99.13% for signature recognition, and deep learning-based Convolutional Neural Networks (CNN) gave an accuracy of 99.14% for handwritten numeric character recognition. This survey paper predicts a promising direction for finding an efficient technique for future research.