Fast two-level HMM decoding algorithm for large vocabulary handwriting recognition

To support large vocabulary handwriting recognition in standard computer platforms, a fast algorithm for hidden Markov model alignment is necessary. To address this problem, we propose a non-heuristic fast decoding algorithm which is based on hidden Markov model representation of characters. The decoding algorithm breaks up the computation of word likelihoods into two levels: state level and character level. Given an observation sequence, the two level decoding enables the reuse of character likelihoods to decode all words in the lexicon, avoiding repeated computation of state sequences. In an 80,000-word recognition task, the proposed decoding algorithm is about 15 times faster than a conventional Viterbi algorithm, while maintaining the same recognition accuracy.