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<Paper uid="W96-0108">
  <Title>A Statistical Approach to Automatic OCR Error Correction in Context</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes an automatic, context-sensitive, word-error correction system based on statistical language modeling (SLM) as applied to optical character recognition (OCR) postprocessing. The system exploits information from multiple sources, including letter n-grams, character confusion probabilities, and word-bigram probabilities. Letter n-grams are used to index the words in the lexicon. Given a sentence to be corrected, the system decomposes each string in the sentence into letter n-grams and retrieves word candidates from the lexicon by comparing string n-grams with lexicon-entry n-grams. The retrieved candidates are ranked by the conditional probability of matches with the string, given character confusion probabilities.</Paragraph>
    <Paragraph position="1"> Finally, the wordobigram model and Viterbi algorithm are used to determine the best scoring word sequence for the sentence. The system can correct non-word errors as well as real-word errors and achieves a 60.2% error reduction rate for real OCR text. In addition, the system can learn the character confusion probabilities for a specific OCR environment and use them in self-calibration to achieve better performance.</Paragraph>
  </Section>
class="xml-element"></Paper>