Efficient OCR Post-Processing Combining Language, Hypothesis and Error Models

  • Rafael Llobet
  • J. Ramon Navarro-Cerdan
  • Juan-Carlos Perez-Cortes
  • Joaquim Arlandis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

In this paper, an OCR post-processing method that combines a language model, OCR hypothesis information and an error model is proposed. The approach can be seen as a flexible and efficient way to perform Stochastic Error-Correcting Language Modeling. We use Weighted Finite-State Transducers (WFSTs) to represent the language model, the complete set of OCR hypotheses interpreted as a sequence of vectors of a posteriori class probabilities, and an error model with symbol substitutions, insertions and deletions. This approach combines the practical advantages of a de-coupled (OCR + post-processor) model with the error-recovery power of a integrated model.

Keywords

Error Model Language Model Posteriori Probability Hypothesis Model Probable Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rafael Llobet
    • 1
  • J. Ramon Navarro-Cerdan
    • 1
  • Juan-Carlos Perez-Cortes
    • 1
  • Joaquim Arlandis
    • 1
  1. 1.Instituto Tecnologico de InformaticaUniversidad Politecnica de ValenciaValenciaSpain

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