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Efficient Automatic OCR Word Validation Using Word Partial Format Derivation and Language Model
In this paper we present an OCR validation module, implemented for the System for Preservation of Electronic Resources (SPER) developed at the U.S. National Library of Medicine. The module detects and corrects suspicious words in the OCR output of scanned textual documents through a procedure of deriving partial formats for each suspicious word, retrieving candidate words by partial-match search from lexicons, and comparing the joint probabilities of N-gram and OCR edit transformation corresponding to the candidates. The partial format derivation, based on OCR error analysis, efficiently and accurately generates candidate words from lexicons represented by ternary search trees. In our test case comprising a historic medico-legal document collection, this OCR validation module yielded the correct words with 87% accuracy and reduced the overall OCR word errors by around 60%.