A hypothesis testing approach to word recognition using an A* search algorithm

An hypothesis testing approach for recognizing machine-printed words is presented in this paper. Based on knowledge of the document font and candidates for the identity of a word, this approach searches a tree of word decisions to generate and test hypotheses for character recognition and segmentation. The search starts at each sequential character position from both ends of a word image and proceeds inward. The accumulated cost of reaching a certain partial recognition decision is combined with the estimate of the potential cost to reach a goal state using an A* search algorithm. The proposed algorithm compensates for local degradations by relying on global characteristics of a word image. Tests of the algorithm show a recognition rate of 98.93% on degraded scanned document images with touching characters.