Csm-Autossociators Combination For Degraded Machine Printed Character Recognition

This paper presents an OCR method that combines the complementary similarity measure (CSM) method with a set of autossociators for degraded character recognition. In the serial combination; the first classifier must achieve lower errors and be very well suited for rejection, whereas the second classifier must allow only low errors and rejects. We introduce a rejection criterion mode used as a quality measurement of the degraded character which makes the CSM-based classifier very powerful and very well suited for rejection We report experimental results for a comparison of three methods: the CSM method, the autoassociator-based classifier and the proposed combined architecture. Experimental results show an achievement of 99.59 % of recognition rate on poor quality bank check characters, which confirm that the proposed approach can be successfully used for effective degraded printed character recognition.