{"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/omniparser-a-unified-framework-for-text-1","title":"OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table Recognition","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Jianqiang Wan","Sibo Song","Wenwen Yu","Yuliang Liu","Wenqing Cheng","Fei Huang","Xiang Bai","Cong Yao","Zhibo Yang"],"abstract":"    Recently visually-situated text parsing (VsTP) has experienced notable advancements driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However due to the diversified targets and heterogeneous schemas previous works usually design task-specific architectures and objectives for individual tasks which inadvertently leads to modal isolation and complex workflow. In this paper we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically we devise a universal model called OmniParser which can simultaneously handle three typical visually-situated text parsing tasks: text spotting key information extraction and table recognition. In OmniParser all tasks share the unified encoder-decoder architecture the unified objective: point-conditioned text generation and the unified input & output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks despite its unified concise design. The code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Wan_OmniParser_A_Unified_Framework_for_Text_Spotting_Key_Information_Extraction_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Wan_OmniParser_A_Unified_Framework_for_Text_Spotting_Key_Information_Extraction_CVPR_2024_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":"omniparser-a-unified-framework-for-text-1","repo_url":"https://github.com/alibabaresearch/advancedliteratemachinery","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"key-information-extraction","task_name":"Key Information Extraction"},{"task_slug":"table-recognition","task_name":"Table Recognition"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"text-spotting","task_name":"Text Spotting"},{"task_slug":"document-understanding","task_name":"document understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}