{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/post-ocr-parsing-building-simple-and-robust","title":"Post-OCR parsing: building simple and robust parser via BIO tagging","arxiv_id":null,"date":"2019-09-14","proceeding":"NeurIPS Workshop Document_Intelligen 2019 12","authors":["Wonseok Hwang","Seonghyeon Kim","Minjoon Seo","Jinyeong Yim","Seunghyun Park","Sungrae Park","Junyeop Lee","Bado Lee","Hwalsuk Lee"],"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.","url_abs":"https://openreview.net/forum?id=SJgjf695UB","url_pdf":"https://openreview.net/pdf?id=SJgjf695UB","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"post-ocr-parsing-building-simple-and-robust","repo_url":"https://github.com/clovaai/cord","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}