{"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/a-tree-structured-decoder-for-image-to-markup","title":"A Tree-Structured Decoder for Image-to-Markup Generation","arxiv_id":null,"date":"2020-01-01","proceeding":"ICML 2020 1","authors":["Jianshu Zhang","Jun Du","Yongxin Yang","Yi-Zhe Song","Si Wei","Li-Rong Dai"],"abstract":"Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup. However, for tree-structured representational markup, string representations can hardly cope with the structural complexity. In this work, we first show via a set of toy problems that string decoders struggle to decode tree structures, especially as structural complexity increases. We then propose a tree-structured decoder that specifically aims at generating a tree-structured markup. Our decoders works sequentially, where at each step a child node and its parent node are simultaneously generated to form a sub-tree. This sub-tree is consequently used to construct the final tree structure in a recurrent manner. Key to the success of our tree decoder is twofold, (i) it strictly respects the parent-child relationship of trees, and (ii) it explicitly outputs trees as oppose to a linear string. Evaluated on both math formula recognition and chemical formula recognition, the proposed tree decoder is shown to greatly outperform strong string decoder baselines.","url_abs":"https://proceedings.icml.cc/static/paper_files/icml/2020/3307-Paper.pdf","url_pdf":"https://proceedings.icml.cc/static/paper_files/icml/2020/3307-Paper.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":"a-tree-structured-decoder-for-image-to-markup","repo_url":"https://github.com/JianshuZhang/TreeDecoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"handwritten-mathmatical-expression","task_name":"Handwritten Mathmatical Expression Recognition"},{"task_slug":"math","task_name":"Math"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/handwritten-mathmatical-expression","task":"Handwritten Mathmatical Expression Recognition","dataset":"CROHME 2014","model":"TD","rank_in_archive_order":16,"of":17,"metrics":{"ExpRate":"49.1"},"uses_additional_data":false},{"leaderboard":"/sota/handwritten-mathmatical-expression-1","task":"Handwritten Mathmatical Expression Recognition","dataset":"CROHME 2016","model":"TD","rank_in_archive_order":14,"of":16,"metrics":{"ExpRate":"48.5"},"uses_additional_data":false},{"leaderboard":"/sota/handwritten-mathmatical-expression-2","task":"Handwritten Mathmatical Expression Recognition","dataset":"CROHME 2019","model":"TD","rank_in_archive_order":13,"of":14,"metrics":{"ExpRate":"51.4"},"uses_additional_data":false},{"leaderboard":"/sota/handwritten-mathmatical-expression-3","task":"Handwritten Mathmatical Expression Recognition","dataset":"HME100K","model":"TD","rank_in_archive_order":11,"of":12,"metrics":{"ExpRate":"62.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}