A keyword spotting approach using blurred shape model-based descriptors

  • Authors:

  • Alicia Fornés;Volkmar Frinken;Andreas Fischer;Jon Almazán;Gabriel Jackson;Horst Bunke

  • Affiliations:

  • Universitat Autònoma de Barcelona, Edifici O, Bellaterra, Spain;Institute of Computer Science and Applied Mathematics, Neubrückstrasse, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, Neubrückstrasse, Bern, Switzerland;Universitat Autònoma de Barcelona, Edifici O, Bellaterra, Spain;Institute of Computer Science and Applied Mathematics, Neubrückstrasse, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, Neubrückstrasse, Bern, Switzerland

  • Venue:
  • Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
  • Year:
  • 2011

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Abstract

The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.