Journal article

A novel word spotting method based on recurrent neural networks.

  • Frinken V Institute of Computer Science and Applied Mathematics (IAM), University of Bern, Neubrückstrasse 10, Bern CH-3012, Switzerland. frinken@iam.unibe.ch
  • Fischer A
  • Manmatha R
  • Bunke H
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  • 2011-06-08
Published in:
  • IEEE transactions on pattern analysis and machine intelligence. - 2012
English Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.
Language
  • English
Open access status
green
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Persistent URL
https://sonar.ch/global/documents/79721
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