Recognition Of Handwritten Month Words On Bank Cheques

This paper describes an off-line system which recognizes unconstrained handwritten month words extracted from Canadian batik cheques. A segmentation based grapheme level HMM (Hidden Markov Model) classifier, and two MLP (Multi-Layer Perceptron) classifiers with different architectures and different features have been developed in CENPARMI for the recognition of month words. In this paper, a combination method with an effective conditional topology is presented, and the most widely used combination rules including Vote, Sum and product are experimented. A new modified Product rule is also proposed, which has produced the best recognition rate of 85.36% when tested on a real-life standard Canadian batik cheque database.