Symbol graph based discriminative training and rescoring for improved math symbol recognition

In the symbol recognition stage of online handwritten math expression recognition, the one-pass dynamic programming algorithm can produce high-quality symbol graphs in addition of the best recognized hypotheses. In this paper, we exploit the rich hypotheses embedded in a symbol graph to discriminatively train the exponential weights of different model likelihoods and the insertion penalty. The training is investigated in two different criteria: maximum mutual information (MMI) and minimum symbol error (MSE). After discriminative training, trigram-based graph rescoring is performed in a post-processing stage. Experimental results finally show a 97% symbol accuracy on a test set of 2,574 written expressions with 43,300 symbols, a significant improvement of symbol accuracy obtained.