{"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/appjsonify-an-academic-paper-pdf-to-json","title":"appjsonify: An Academic Paper PDF-to-JSON Conversion Toolkit","arxiv_id":"2310.01206","date":"2023-10-02","proceeding":null,"authors":["Atsuki Yamaguchi","Terufumi Morishita"],"abstract":"We present appjsonify, a Python-based PDF-to-JSON conversion toolkit for academic papers. It parses a PDF file using several visual-based document layout analysis models and rule-based text processing approaches. appjsonify is a flexible tool that allows users to easily configure the processing pipeline to handle a specific format of a paper they wish to process. We are publicly releasing appjsonify as an easy-to-install toolkit available via PyPI and GitHub.","url_abs":"https://arxiv.org/abs/2310.01206v2","url_pdf":"https://arxiv.org/pdf/2310.01206v2.pdf","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":"appjsonify-an-academic-paper-pdf-to-json","repo_url":"https://github.com/hitachi-nlp/appjsonify","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"document-layout-analysis","task_name":"Document Layout Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}