A PROBABILISTIC MODEL FOR CANDIDATE SELECTION IN RECOGNITION OF LARGE CHARACTER SET

In this paper, a probabilistic model is proposed for precise candidate selection in recognition of large character set. This model explores the redundancy of minimum distance classification and selects a reduced set of candidate classes. Firstly, the output class probabilities are evaluated from rank ordered distances by minimizing the square error of probability ratio. Then the classifier behavior knowledge is incorporated by diagnostic inference to deduce the input class probabilities. Based on the inferred input probabilities, the candidate set is determined by thresholding of probability ratio. The efficiency of this method was demonstrated in coarse recognition of ETL8B2 and ETL9B databases. Compared to minimum distance classification with fixed number of candidates, the proposed method selects less than half of candidates with precision preserved.