Using confusing characters to improve character recognition rate

Abstract

This paper proposes a confusion set analysis approach to increasing character recognition rate. In the training phase, top five candidates are outputted from an OCR system. Each candidate is assigned a weight in reversed-rank. If the reversed-rank sum of an output candidate is greater than a threshold, the input character is stored in the confusion set of the output candidate. In the recognition phase, the characters in the confusion set with respect to the output character are recognized by another OCR system to find the final recognition result.