@inproceedings{feng-etal-2025-dolphin,
title = "Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting",
author = "Feng, Hao and
Wei, Shu and
Fei, Xiang and
Shi, Wei and
Han, Yingdong and
Liao, Lei and
Lu, Jinghui and
Wu, Binghong and
Liu, Qi and
Lin, Chunhui and
Tang, Jingqun and
Liu, Hao and
Huang, Can",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1130/",
doi = "10.18653/v1/2025.findings-acl.1130",
pages = "21919--21936",
ISBN = "979-8-89176-256-5",
abstract = "Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specialized expert models or directly generate page-level content autoregressively, facing integration overhead, efficiency bottlenecks, and layout structure degradation despite their decent performance. To address these limitations, we present \textit{Dolphin} (\textit{ \textbf{Do}cument Image \textbf{P}arsing via \textbf{H}eterogeneous Anchor Prompt\textbf{in}g}), a novel multimodal document image parsing model following an analyze-then-parse paradigm. In the first stage, Dolphin generates a sequence of layout elements in reading order. These heterogeneous elements, serving as anchors and coupled with task-specific prompts, are fed back to Dolphin for parallel content parsing in the second stage. To train Dolphin, we construct a large-scale dataset of over 30 million samples, covering multi-granularity parsing tasks. Through comprehensive evaluations on both prevalent benchmarks and self-constructed ones, Dolphin achieves state-of-the-art performance across diverse page-level and element-level settings, while ensuring superior efficiency through its lightweight architecture and parallel parsing mechanism. The code and pre-trained models are publicly available at https://github.com/ByteDance/Dolphin"
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<abstract>Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specialized expert models or directly generate page-level content autoregressively, facing integration overhead, efficiency bottlenecks, and layout structure degradation despite their decent performance. To address these limitations, we present Dolphin ( Document Image Parsing via Heterogeneous Anchor Prompting), a novel multimodal document image parsing model following an analyze-then-parse paradigm. In the first stage, Dolphin generates a sequence of layout elements in reading order. These heterogeneous elements, serving as anchors and coupled with task-specific prompts, are fed back to Dolphin for parallel content parsing in the second stage. To train Dolphin, we construct a large-scale dataset of over 30 million samples, covering multi-granularity parsing tasks. Through comprehensive evaluations on both prevalent benchmarks and self-constructed ones, Dolphin achieves state-of-the-art performance across diverse page-level and element-level settings, while ensuring superior efficiency through its lightweight architecture and parallel parsing mechanism. The code and pre-trained models are publicly available at https://github.com/ByteDance/Dolphin</abstract>
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%0 Conference Proceedings
%T Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
%A Feng, Hao
%A Wei, Shu
%A Fei, Xiang
%A Shi, Wei
%A Han, Yingdong
%A Liao, Lei
%A Lu, Jinghui
%A Wu, Binghong
%A Liu, Qi
%A Lin, Chunhui
%A Tang, Jingqun
%A Liu, Hao
%A Huang, Can
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F feng-etal-2025-dolphin
%X Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specialized expert models or directly generate page-level content autoregressively, facing integration overhead, efficiency bottlenecks, and layout structure degradation despite their decent performance. To address these limitations, we present Dolphin ( Document Image Parsing via Heterogeneous Anchor Prompting), a novel multimodal document image parsing model following an analyze-then-parse paradigm. In the first stage, Dolphin generates a sequence of layout elements in reading order. These heterogeneous elements, serving as anchors and coupled with task-specific prompts, are fed back to Dolphin for parallel content parsing in the second stage. To train Dolphin, we construct a large-scale dataset of over 30 million samples, covering multi-granularity parsing tasks. Through comprehensive evaluations on both prevalent benchmarks and self-constructed ones, Dolphin achieves state-of-the-art performance across diverse page-level and element-level settings, while ensuring superior efficiency through its lightweight architecture and parallel parsing mechanism. The code and pre-trained models are publicly available at https://github.com/ByteDance/Dolphin
%R 10.18653/v1/2025.findings-acl.1130
%U https://aclanthology.org/2025.findings-acl.1130/
%U https://doi.org/10.18653/v1/2025.findings-acl.1130
%P 21919-21936
Markdown (Informal)
[Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting](https://aclanthology.org/2025.findings-acl.1130/) (Feng et al., Findings 2025)
ACL
- Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, and Can Huang. 2025. Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21919–21936, Vienna, Austria. Association for Computational Linguistics.