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 [1]. 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.