Abstract—Automatic Cheque Processing is one of the most widely researched areas in document analysis and biometric. Various methodologies have been proposed in this area for Automatic Cheque Processing and forgery detection. An account holder gives cheques to another person as account payee or self-cheque. It is been observed that a number of forgery cases have been registered as cheque forgery, where some person has forged the signature of another person and provided a self-cheque to himself. In this paper we propose a mechanism for recognition of cheque fields, like name, amount and also verify the signature and it’s authenticity. We propose a unique two stage model of Automatic Cheque processing with detecting skilled forgery in the signature by combining two feature types namely Sum graph and HMM and classify them with knowledge based classifier and probability neural network. We proposed a unique technique of using HMM as feature rather than a classifier as being widely proposed by most of the authors in signature recognition. Results show a higher false rejection than false acceptance rate. Character segmentation accuracy is found to be 95%, character recognition efficiency 83%, Digit recognition efficiency is 91%.and system detects forgeries with an accuracy of 80% and can detect the signatures with 91% accuracy.

Index Terms—Nearest Mean Classifier, PNN, Biometric, Sum Graph, HMM, Forgery Detection

Mohit Mehta is with MIT Mandsaur, India as lecturer, Tel:0091-9981393677, email: mohit.mehta@mitmandsaur.info
Rupesh Sancheti is with MIT Mandsaur, India as lecturer, Tel:0091-9827213991, email: rupesh.sancheti@mitmandsaur.info
Ajay Marchya is with MIT Mandsaur, India as lecturer, email: ajay.marchya@mitmandsaur.info

Cite: Mohit Mehta, Rupesh Sanchati and Ajay Marchya, "Automatic Cheque Processing System," International Journal of Computer and Electrical Engineering vol. 2, no. 4, pp. 761-765, 2010.