Phrase-based correction model for improving handwriting recognition accuracies

Authors:
Faisal Farooq;Damien Jose;Venu Govindaraju
Affiliations:
Center for Unified Biometrics and Sensors, State University of New York at Buffalo, Amherst, NY 14228, USA;Center for Unified Biometrics and Sensors, State University of New York at Buffalo, Amherst, NY 14228, USA;Center for Unified Biometrics and Sensors, State University of New York at Buffalo, Amherst, NY 14228, USA
Venue:
Pattern Recognition
Year:
2009

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Abstract

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.