Efficient postprocessing algorithms for error correction in handwritten Hangul address and human name recognition

In this paper, we propose efficient postprocessing algorithms for error correction in handwritten Hangul (Korean script) address and human name recognition. As the load on the character recognizer for the recognition of the administrative district part of addresses was reduced by restricting the number of candidate characters to be matched by using an address lexicon, the processing speed and recognition rate were greatly improved. Also, as the misrecognition results from the character recognizer were corrected by efficient postprocessing algorithms based on backtracking, a high recognition rate of more than 98% could be obtained for the administrative district part of input addresses. For the recognition of human name part, misrecognition could be effectively corrected by combining the a priori probability and the confusion probability of each character which composes human names.