Pair-wise discrimination based on a stroke importance measure
The pair-wise discriminator is a binary classifier that verifies the outcome of the recognizer if it belongs to a class in a pre-defined confusion pair database. It is difficult to discriminate a pair of characters that are very similar in shape except for a small difference, because the small difference can be overridden by the writing variation. This paper proposes a pair-wise discrimination method that discriminates similar characters by focusing on the structural difference between the two characters. It discriminates a pair of characters by comparing their matching scores between the input character and the models of the two characters. When the stroke matching scores are combined to compute the overall matching score, each stroke is assigned a weight to reflect its importance in discriminating the character pair. By assigning large weights to the discriminative strokes, the difference between the characters is emphasized. The stroke weights are systematically obtained by a neural network training algorithm. In the experiments, the recognition performance was significantly improved by applying the proposed method.
pair-wise discrimination stroke weighting neural network character recognition postprocessing