Handwritten word-spotting using hidden Markov models and universal vocabularies

✍ Scribed by José A. Rodríguez-Serrano; Florent Perronnin

• Publisher: Elsevier Science
• Year: 2009
• Tongue: English
• Weight: 659 KB
• Volume: 42
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2009.02.005

✦ Synopsis

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 wordimage 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.