ScholarWorks@중앙대학교

초록

The development of Optical Character Recognition (OCR) has made it possible to digitize analog documents. It shows very high recognition accuracy for standardized documents. However, OCR errors still occur frequently in complex documents. To resolve these issues, an OCR error correction procedure is required. The majority of OCR errors are repeated for the same characters. Accordingly, OCR error information has an important meaning in OCR error correction work. However, there are few studies utilizing OCR error information. In order to identify patterns, this study examines OCR error data. It then suggests an OCR mistake correction technique based on neural machine translation. Experiments were carried out using the English dataset from the ICDAR 2017/2019 Post-OCR text correction competition in order to validate the proposed method. The experimental results showed that the model using OCR error information demonstrated a higher improvement rate than the model without OCR error information. It also showed up to 8%P improved results compared to the existing state of the art.

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