Handwritten word-spotting using hidden Markov models and universal vocabularies

  • Authors:

  • José A. Rodríguez-Serrano;Florent Perronnin

  • Affiliations:

  • Centre de Visió Per Computador (CVC), Universitat Autònoma de Barcelona, Edifici O Campus Bellaterra, 08193 Bellaterra, Spain;Xerox Research Centre Europe, 6 Chemin de Maupertuis, 38240 Meylan, France

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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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.