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

Proposes 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 in addresses was reduced by restricting the candidate characters to be matched based on a hierarchical address lexicon, the processing speed and recognition rate were greatly improved. Also, the misrecognition results from the character recognizer were corrected by using efficient postprocessing algorithms based on backtracking. For the recognition of the human name part, misrecognition of human names could be effectively corrected by combining the a priori probability and the confusion probability of each character making up the human names