In online handwritten math expression recognition, one-pass dynamic programming can produce high-quality symbol graphs in addition to best symbol sequence hypotheses, especially after discriminative training and trigram graph rescoring. Impact of symbol graphs on whole expression recognition, however, has not been referred to yet, since the interface of structure analysis module does not work well with symbol graphs on the basis of typical tree search. In this paper, we propose a method to convert symbol graph to segment graph to make the tree search efficient and effective, i.e., search of best segmentations in symbol graph without pruning becomes possible. With trigram rescoring, the overall expression recognition accuracy has been improved by 10% relative in comparison with the baseline.<\/p>\n","protected":false},"excerpt":{"rendered":"
In online handwritten math expression recognition, one-pass dynamic programming can produce high-quality symbol graphs in addition to best symbol sequence hypotheses, especially after discriminative training and trigram graph rescoring. Impact of symbol graphs on whole expression recognition, however, has not been referred to yet, since the interface of structure analysis module does not work well 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