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Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

T0 review · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A two-stage model that analyzes page layout first, then parses text, tables, and formulas in parallel, reports state-of-the-art accuracy and speed on the benchmarks it evaluates.

arxiv 2505.14059 v1 pith:EZLI4UWJ submitted 2025-05-20 cs.CV

Hao Feng , Shu Wei , Xiang Fei , Wei Shi , Yingdong Han , Lei Liao , Jinghui Lu , Binghong Wu
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Qi Liu Chunhui Lin Jingqun Tang Hao Liu Can Huang
This is my paper · ORCID
classification cs.CV
keywords dolphinparsingimagetextbfdocumentelementsanchorcontent
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Dolphin is a document parser: it turns a picture of a page into structured text, tables, and formulas. Most modern parsers either chain together separate expert systems (one for layout, one for tables, one for math) or use a single large vision-language model that writes the whole page from top to bottom. The first approach is slow and hard to integrate; the second loses layout and gets slow on long pages. Dolphin does both jobs in one small model, but in two passes. In the first pass it reads the page and outputs a list of elements in reading order, such as a figure at these coordinates, then a caption, then a table, then a paragraph. In the second pass it crops each element out of the original image and parses those crops in parallel, using a different instruction for tables (produce HTML) and for text or formulas (produce plain text or LaTeX). Because the crops are small and independent, many can be processed at once, which is where the speed comes from. The authors trained the model on about 30 million synthetic and real samples, spanning HTML, LaTeX, markdown, tables, and formulas. On the benchmarks they report, Dolphin gets lower edit distance (fewer errors) than GPT-4o, Claude, and specialized OCR models on full pages, and it leads on formula and table recognition. The model has 322 million parameters, much smaller than the general-purpose models it is compared with. The main caveat is that part of the evaluation uses benchmarks the authors built themselves, and the paper does not state whether the public table benchmark test sets were kept out of the 30-million-sample training set.
Extended reading notes

Core claim

Abstract: '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.' Concretely, Table 1 reports edit distances of 0.0114 and 0.0131 on Fox-Page EN/ZH and 0.1028 on Dolphin-Page, at 0.1729 FPS, outperforming all listed baselines. If correct, a 322M-parameter two-stage model can parse document pages more accurately and faster than much larger autoregressive VLMs.

Load-bearing premise

The evaluation assumes train/test disjointness: Section 4.1 lists PubTabNet and PubTab1M as table training data and Section 4.2 evaluates on the same benchmark families without stating that official test splits were excluded. It also introduces Dolphin-Page and Dolphin-Block as self-constructed benchmarks without stating that their pages and paragraphs are disjoint from the 30M-sample training corpus. If these overlaps exist, the SOTA page-level and table results reduce to fitting the evaluation distribution.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free numeric constants are hand-fitted in a derivation; the 322M model weights are learned. The empirical claims depend on three domain assumptions: benchmark split integrity, synthetic-to-real transfer, and validity of the self-constructed benchmarks. No invented entities are introduced; 'anchor prompting' names a procedure, not a new object.

assumptions (4)
  • domain assumption Official test splits of PubTabNet, PubTab1M, and formula benchmarks are excluded from the 30M-sample training set.
    Section 4.1 says table training uses PubTabNet and PubTab1M; Section 4.2 evaluates on the same benchmark names without mentioning split exclusion. This assumption is load-bearing for the reported table and formula scores.
  • domain assumption Synthetic documents rendered from HTML, LaTeX, and Markdown transfer to real-world documents.
    Section 4.1 constructs 5.58M page samples synthetically; the page-level SOTA claim assumes these renderings are representative of natural pages.
  • domain assumption Dolphin-Page and Dolphin-Block are unbiased, well-annotated benchmarks.
    Section 4.2 introduces them; no annotation protocol, inter-annotator agreement, or disjointness from training data is stated.
  • domain assumption A single 322M-parameter encoder-decoder with Swin and mBart can represent all needed document parsing skills.
    Sections 3.2 and 3.3 choose this architecture with no analysis of capacity trade-offs; all results depend on it.

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Pith. "Pith review of Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting." pith.science (2026). https://pith.science/paper/EZLI4UWJ

@misc{pith2026250514059,
  author       = {Pith},
  title        = {Pith review of: Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EZLI4UWJ}},
  note         = {Machine review of arXiv:2505.14059}
}
read the original 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

Figures

Figures reproduced from arXiv: 2505.14059 by the authors.

Figure 1
Figure 1. Comparison of Dolphin with advanced VLMs [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 4
Figure 4. FPN for object segment proposals. The feature pyramid is constructed with identical structure as for object detection. We apply a small MLP on $5 \times 5$ windows to generate dense object segments with output dimension of $14 \times 14$. Shown in orange are the size of the image regions the mask corresponds to for each pyramid level (levels $P_{3-5}$ are shown here). Both the corresponding image region size (light … view at source ↗
Figure 3
Figure 3. Visualization of Dolphin’s page-level parsing results. Left: Layout analysis form Stage 1 with predicted element boundaries and reading order. Middle: Element-specific parsing outputs from Stage 2. Right: Final rendered document in markdown format. More cases are shown in the supplementary material. of data for different training objectives. Mixed Documents. We collect 0.12M docu￾ments from diverse sources, includin… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Demonstration of Dolphin’s element-level parsing across diverse scenarios. Input images are shown in the top row, with corresponding recognition results in the bottom row. Left: Text paragraph parsing in complex layouts. Middle: Bilingual text paragraph recognition. Ri…
Figure 1
Figure 1. Figure 1: Our MAE architecture. During pre-training, a large random subset of image patches ( e.g., 75 % ) is masked out. The encoder is applied to the small subset of visible patches. Mask tokens are introduced after the encoder, and the full set of en￾coded patches and mask to…
Figure 5
Figure 5. Figure 5: Additional capabilities of Dolphin. Left: Parsing the text content from a given bounding box region. Right: Text spotting results showing detected text lines (visualized in the image) and their content. We visualize three representative cases in Fig￾ure 3, showing the …
Figure 6
Figure 6. Figure 6: A case study demonstrating the effectiveness [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Examples of synthetic training data generated from different source formats. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Visualization of Dolphin’s page-level parsing results. Left: Layout analysis form Stage 1 with predicted element boundaries and reading order. Middle: Element-specific parsing outputs from Stage 2. Right: Final rendered document in markdown format. 15 [PITH_FULL_IMAGE…
Figure 9
Figure 9. Figure 9: Visualization of Dolphin’s page-level parsing results. Left: Input text-rich images including mobile phone screenshots, shopping receipts, and webpage captures. Middle: Layout analysis form Stage 1 with predicted element boundaries and reading order. Right: Final rende…
Figure 10
Figure 10. Figure 10: Visualization of Dolphin’s formula parsing results. From top to bottom, we show three formula types: inline formula, single-line block formula, and multi-line block formula. For each case, we visualize the complete parsing pipeline: input formula image (top), LaTeX pa…
Figure 11
Figure 11. Figure 11: Visualization of Dolphin’s table parsing capability. Top: Input large-scale table image containing hundreds of cells. Bottom: Rendered HTML table based on Dolphin’s parsing result. This example demonstrates Dolphin’s strong ability in handling large-scale structured t…

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Forward citations

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