Handwritten Text Recognition in Bank Cheques

In spite of rapid evolution of digital technologies, a huge number of applications still rely on the use of paper based medium. This is especially true for processing of bank cheques. The pre-printed account number and cheque number might be easily readable and processed automatically. However, the handwritten texts in cheque are usually processed manually involving important time and cost. An attempt has been made in this paper to develop a bank cheque handwritten text recognition (BCHWTR) system for cheques of Indian banks by recognizing the handwritten characters present in the 'payee name', 'courtesy amount (both in words and figures)' and 'date' fields by using image processing techniques on handwritten cheque images. Images of bank cheques are fed as input to the proposed system. There are four stages in the proposed system: cropping the image at a specific location; segmentation of handwritten lines, words and characters; feature extraction from individual characters and digits using Histogram of Oriented Gradients (HOG) method and Grey Level Co-occurrence Matrix (GLCM) texture features; recognition of isolated characters and digits using the Support Vector Machine (SVM) based classification process that ensures correct recognition. The performance of present BCHWTR system is evaluated on a self-generated dataset of bank cheques and it has provided a promising result.