On-line handwriting recognition with constrained N-best decoding

It is well known that N-best decoding for speech recognition coupled with post-processing can provide significant accuracy advantages. We have implemented and experimented with N-best decoding for handwriting recognition, using an N-best decoding algorithm that employs a synchronous forward pass and an asynchronous backward pass. One novel aspect of our algorithm is the use of pruning in the backward pass to constrain the search to candidates whose likelihood score is within a threshold specified using the likelihood score of the best candidate. We show that this algorithm is more efficient than traditional N-best decoding algorithms. A two-stage method is introduced in which the language model changes from a relaxed model during the N-best search to a more constrained model for rescoring in a second pass. This method reduces the computation needed for more detailed pattern matching by preselecting the N-best most likely candidates