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.
Work partially supported by the Spanish MICINN grants TIN2009-14205-C04-02 and Consolider Ingenio 2010: MIPRCV (CSD2007-00018) and by IMPIVA and the E.U. by means of the ERDF in the context of the R+D Program for Technological Institutes of IMPIVA network for 2010 (IMIDIC_2009/204).
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Llobet, R., Navarro-Cerdan, J.R., Perez-Cortes, JC., Arlandis, J. (2010). Efficient OCR Post-Processing Combining Language, Hypothesis and Error Models. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_72
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