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Using topic models for OCR correction

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Abstract

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.

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Authors and Affiliations

  1. Image and Knowledge Management, Siemens Medical Solutions, Malvern, PA, USA

    Faisal Farooq

  2. Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA

    Anurag Bhardwaj & Venu Govindaraju

Authors
  1. Faisal Farooq
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  2. Anurag Bhardwaj
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  3. Venu Govindaraju
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Correspondence to Faisal Farooq.

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Farooq, F., Bhardwaj, A. & Govindaraju, V. Using topic models for OCR correction. IJDAR 12, 153–164 (2009). https://doi.org/10.1007/s10032-009-0095-7

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  • DOI: https://doi.org/10.1007/s10032-009-0095-7

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