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Optical character recognition (OCR) systems differ in the types of errors they make, particularly in recognizing characters from degraded or poor quality documents. The problem is how to correct these OCR errors, which is the first step toward more effective use of the documents in digital libraries. This paper demonstrates the degree to which the word error rate (WER) can be reduced using a decision...
This paper presents a novel method for improving optical character recognition (OCR). The method employs the progressive alignment of hypotheses from multiple OCR engines followed by final hypothesis selection using maximum entropy classification methods. The maximum entropy models are trained on a synthetic calibration data set. Although progressive alignment is not guaranteed to be optimal, the...
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