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Use of Lexicon Density in Evaluating Word Recognizers

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Multiple Classifier Systems (MCS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1857))

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Abstract

We have developed the notion of lexicon density as the true metric to measure expected recognizer accuracy. This metric has a variety of applications, among them evaluation of recognition results, static or dynamic recognizer selection, or dynamic combination of recognizers. We show that the performance of word recognizers increases as lexicon density decreases and that the relationship between the performance and lexicon density is independent of lexicon size. Our claims are supported by extensive experimental validation data.

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Reference

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Authors and Affiliations

  1. Center for Excellence in Document Analysis and Recognition (CEDAR) Department of Computer Science & Engineering, University at Buffalo, Amherst, NY, 14228, USA

    Petr Slavík & V. Govindaraju

Authors
  1. Petr Slavík
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  2. V. Govindaraju
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© 2000 Springer-Verlag Berlin Heidelberg

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Slavík, P., Govindaraju, V. (2000). Use of Lexicon Density in Evaluating Word Recognizers. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_30

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  • DOI: https://doi.org/10.1007/3-540-45014-9_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67704-8

  • Online ISBN: 978-3-540-45014-6

  • eBook Packages: Springer Book Archive

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