Toward island-of-reliability-driven very-large-vocabulary on-line handwriting recognition using character confidence scoring

We explore a novel approach for handwriting recognition tasks whose intrinsic vocabularies are too large to be applied directly as constraints during recognition. Our approach makes use of vocabulary constraints, and addresses the issue that some parts of words may be written more recognizably than others. An initial pass is made with an HMM recognizer, without vocabulary constraints, generating a lattice of character-hypothesis arcs representing likely segmentations of the handwriting signal. Arc confidence scores are computed using a posteriori probabilities. The most-confidently-recognized characters are used to filter the overall vocabulary, generating a word subset manageable for constraining a second recognition pass. With a vocabulary of 273,000 words, we can limit to 50,000 words in the second pass and eliminate 39.3% of the word errors made by a onepass recognizer without vocabulary constraints, and 18.3% of errors made using a fixed 30,000-word set.