Post-OCR parsing: building simple and robust parser via BIO tagging
Wonseok Hwang, Seonghyeon Kim, Minjoon Seo, Jinyeong Yim, Seunghyun Park, Sungrae Park, Junyeop Lee, Bado Lee, Hwalsuk Lee
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Abstract
Parsing textual information embedded in images is important for various down- stream tasks. However, many previously developed parsers are limited to handling the information presented in one dimensional sequence format. Here, we present Post Ocr Tagging based parser (POT), a simple and robust parser that can parse visually embedded texts by BIO-tagging the output of optical character recognition (OCR) task. Our shallow parsing approach enables building robust neural parser with less than a thousand labeled data. POT is validated on receipt and namecard parsing tasks.