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Jose Antonio Rodriguez, & Florent Perronnin. (2008). Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 188–198). LNCS.
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Jose Antonio Rodriguez, & Florent Perronnin. (2009). Handwritten word-spotting using hidden Markov models and universal vocabularies. PR - Pattern Recognition, 42(9), 2103–2116.
Abstract: Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians.
Keywords: Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition
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Jose Antonio Rodriguez, & Florent Perronnin. (2008). Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents. In International Conference on Frontiers in Handwriting Recognition (7–12).
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Jose Antonio Rodriguez, & Florent Perronnin. (2008). Score Normalization for Hmm-based Word Spotting Using Universal Background Model. In International Conference on Frontiers in Handwriting Recognition (82–87).
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