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Phrase-based correction model for improving handwriting recognition accuracies
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DOI：10.1016/j.patcog.2008.12.014.png)
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Abstract
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We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a phrase-based system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Post-processing
Noisy channel
Handwriting recognition
Error correction
Viterbi decoding
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