Optical Character Recognition on Bank Cheques Using 2D Convolution Neural Network

Banking system worldwide suffers from huge dependencies upon manpower and written documents thus making conventional banking processes tedious and time-consuming. Existing methods for processing transactions made through cheques causes a delay in the processing as the details have to be manually entered. Optical Character Recognition (OCR) finds usage in various fields of data entry and identification purposes. The aim of this work is to incorporate machine learning techniques to automate and improve the existing banking processes, which can be achieved through automatic cheque processing. The method used is Handwritten Character Recognition where pattern recognition is clubbed with machine learning to design an Optical Character Recognizer for digits and capital alphabets which can be both printed and handwritten. The Extension of Modified National Institute of Standards and Technology (EMNIST) dataset, a standard dataset for alphabets and digits is used for training the machine learning model. The machine learning model used is 2D Convolution Neural Network which fetched a training accuracy of 98% for digits and 97% for letters. Image processing techniques such as segmentation and extraction are applied for cheque processing. Otsu thresholding, a type of global thresholding is applied on the processed output. The processed segmented image of each character is fed to the trained model and the predicted results are obtained. From a pool of sample cheques that were used for testing an accuracy of 95.71% was achieved. The idea of combining convolution neural network with image processing techniques on bank cheques is novel and can be deployed in banking sectors.