Top-Down Likelihood Word Image Generation Model for Holistic Word Recognition

  • Eiki Ishidera
  • Simon M. Lucas
  • Andrew C. Downton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2423)


This paper describes a new top-down word image generation model for word recognition. This model can generate a word image with a likelihood based on linguistic knowledge, segmentation and character image. In the recognition process, first, the model generates the word image which approximates an input image best for each of a dictionary of possible words. Next, the model calculates the distance value between the input image and each generated word image. Thus, the proposed method is a type of holistic word recognition method. The effectiveness of the proposed method was evaluated in an experiment using type-written museum archive card images. The difference between a non-holistic method and the proposed method is shown by the evaluation. The small errors accumulate in non-holistic methods during the process carried out, because the non-holistic methods can’t cover the whole word image but only part images extracted by segmentation, and the non-holistic method can’t eliminate the blackpixels intruding in the recognition window from neighboring characters. In the proposed method, we can expect that no such errors will accumulate. Results show that a recognition rate of 99.8% was obtained, compared with only 89.4% for a recently published comparator algorithm.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Eiki Ishidera
    • 1
  • Simon M. Lucas
    • 2
  • Andrew C. Downton
    • 3
  1. 1.NEC CorporationMultimedia Research LabsKawasakiJapan
  2. 2.Dept. of Computer ScienceUniversity of EssexColchesterUK
  3. 3.Dept. of Electronic Systems EngineeringUniversity of EssexColchesterUK

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