FinixDocBench

This repository contains a compliance-reviewed public subset of FinixDocBench, the financial-domain document parsing benchmark introduced in the technical report "FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks".

The benchmark focuses on document parsing conditions that are common in real financial workflows but underrepresented in saturated clean-document benchmarks: digitally native insurance clauses, noisy camera-captured medical receipts, ultra-long pages, and very large dense tables. The expected model outputs are page-level Markdown and, where available, structured JSON layout annotations.

Project links:

• Project page: https://finix.alipay.com/
• Hugging Face dataset: https://huggingface.co/datasets/inclusionAI/FinixDocBench
• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Released Subset Contents

This release contains 742 page samples from the broader FinixDocBench benchmark. Track 3 is split into two directories so that ultra-long pages and large-table pages can be evaluated separately.

Track	Directory	Source type	Pages	Files per sample	Main task
FinixDigital	track1_finixdigital_242_insurance_terms/	Digitally native insurance terms	242	image + Markdown + JSON	Markdown parsing and structured layout parsing
FinixPhoto	track2_finixphoto_300/	Mobile-captured medical receipts	300	image + Markdown + JSON	Robust Markdown parsing and structured layout parsing
FinixHuge-Long	track3_finixhuge_100_long/	Ultra-long financial or insurance pages	100	image + Markdown	Ultra-large page Markdown parsing
FinixHuge-Table	track3_finixhuge_100_table/	Large dense table pages	100	image + Markdown	Ultra-large table reconstruction

The full FinixDocBench described in the technical report also includes a larger internal evaluation track, FinixInner, which is not included in this release because of privacy and compliance constraints. The FinixDigital package here is a 242-page insurance-terms subset of the broader FinixDigital track discussed in the report.

Repository Structure

FinixDocBench/
  README.md
  LICENSE.md
  CITATION.cff
  dataset_manifest.jsonl
  metadata.jsonl
  track1_finixdigital_242_insurance_terms/
    images/
    mds/
    jsons/
  track2_finixphoto_300/
    images/
    mds/
    jsons/
  track3_finixhuge_100_long/
    images/
    mds/
  track3_finixhuge_100_table/
    images/
    mds/
  FinixDocBench_Eval_for_Markdown/
    README.md
    requirements.txt
    run_eval.py
    finixdoc_md_eval/

Each sample is matched by file stem. For example, abc123.png, abc123.md, and abc123.json describe the same page when all three files are present.

The dataset_manifest.jsonl file provides one row per sample with relative paths, track metadata, image dimensions, and basic annotation counts. It is intended as a lightweight index for users who want to load the release programmatically.

Tasks

This FinixDocBench release supports three complementary task settings.

1. Full-Page Markdown Parsing

Given a page image, a model should produce a complete page-level Markdown reconstruction. This task is available for all public tracks.

The Markdown ground truth preserves text order, headings, tables, and other page-level structure. HTML <table> blocks are used where table structure, merged cells, or dense financial layouts need to be represented more faithfully than plain Markdown tables.

2. Structured Layout Parsing

Given a page image, a model should produce structured page elements with category labels, bounding boxes, transcribed content, and reading order. This task is available for FinixDigital and FinixPhoto, which include jsons/ annotations.

The public JSON files use pixel-space bounding boxes in the original image coordinate system. Each JSON file includes page metadata plus a layout list.

3. Ultra-Large Page Processability

FinixHuge-Long and FinixHuge-Table evaluate whether a system can return a syntactically valid, non-empty, page-level Markdown result for oversized documents. These pages stress page resolution, output length, table complexity, and reading-order preservation.

Because FinixHuge is Markdown-only in this release, it is best evaluated with Markdown metrics plus a success-rate style processability check.

Annotation Schema

FinixDigital and FinixPhoto use a unified 10-class page-element schema:

page-header
page-footer
title
section-header
text
table
figure
caption
footnote
other

Top-level JSON fields:

Field	Description
width	Original page image width in pixels.
height	Original page image height in pixels.
resized_width	Width used by the annotation or preprocessing pipeline.
resized_height	Height used by the annotation or preprocessing pipeline.
max_pixels	Maximum pixel budget recorded by the preprocessing pipeline.
min_pixels	Minimum pixel budget recorded by the preprocessing pipeline.
layout	Ordered list of page elements.

Each layout item contains:

Field	Description
category	One of the 10 page-element labels.
bbox	Pixel-space bounding box [x1, y1, x2, y2] in the page image coordinate system.
content	Transcribed text, Markdown structural marker, or serialized table content. This field may be absent for some figure elements.
order	Reading-order index of the layout element.

Example:

{
  "width": 993,
  "height": 1404,
  "resized_width": 992,
  "resized_height": 1408,
  "max_pixels": 16777216,
  "min_pixels": 4096,
  "layout": [
    {
      "category": "section-header",
      "bbox": [82, 364, 223, 394],
      "content": "## 2.3 责任免除",
      "order": 5
    },
    {
      "category": "table",
      "bbox": [337, 156, 916, 295],
      "content": "<table>...</table>",
      "order": 3
    }
  ]
}

Dataset Statistics

Track	Images	Markdown files	JSON files	Notes
FinixDigital	242	242	242	6,223 structured layout elements; 214 tables
FinixPhoto	300	300	300	8,517 structured layout elements; 224 tables
FinixHuge-Long	100	100	0	Ultra-long page images, up to 287M pixels
FinixHuge-Table	100	100	0	Large dense table images, up to 386M pixels
Total	742	742	542	All samples have paired images and Markdown

Category counts for the structured JSON tracks:

Category	Count
text	10,158
section-header	1,522
figure	1,307
title	504
table	438
caption	265
page-footer	223
footnote	204
page-header	64
other	55

Loading Examples

Load the manifest with the Hugging Face datasets library:

from datasets import load_dataset

manifest = load_dataset(
    "json",
    data_files="dataset_manifest.jsonl",
    split="train",
)

print(manifest[0])

Read a sample locally after cloning the repository:

from pathlib import Path
from PIL import Image
import json

repo = Path("FinixDocBench")
row = manifest[0]

image = Image.open(repo / row["image_path"])
markdown = (repo / row["markdown_path"]).read_text(encoding="utf-8")

annotation = None
if row["json_path"] is not None:
    annotation = json.loads((repo / row["json_path"]).read_text(encoding="utf-8"))

Evaluation

The repository includes a lightweight Markdown evaluator:

cd FinixDocBench_Eval_for_Markdown
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run evaluation on a track by providing a ground-truth Markdown directory and a prediction Markdown directory with matching file names:

python run_eval.py \
  --gt_dir ../track2_finixphoto_300/mds \
  --pred_dir /path/to/predicted_mds \
  --output_json outputs/finixphoto_result.json

The Markdown evaluator reports:

Metric	Direction	Description
text_block_Edit_dist	Lower is better	Normalized edit distance over matched text blocks.
reading_order_Edit_dist	Lower is better	Normalized edit distance over serialized reading-order sequences.
table_TEDS	Higher is better	Tree-edit-distance-based table similarity, scaled to 0-100.
overall	Higher is better	Composite score on a 0-100 scale.

The overall score is:

overall = ((1 - text_block_Edit_dist) * 100
         + (1 - reading_order_Edit_dist) * 100
         + table_TEDS) / 3

For FinixHuge, users should additionally report a success rate: the fraction of pages for which the system returns a syntactically valid, non-empty page-level Markdown result without runtime failure, severe truncation, or format errors that prevent downstream evaluation.

Structured JSON annotations are provided for FinixDigital and FinixPhoto. This repository currently ships the Markdown evaluator; if you report structured layout metrics, please describe the evaluator, matching rules, and coordinate convention used.

Reference Results from the Technical Report

The following values are copied from the FinixDoc technical report for context. They correspond to the benchmark protocol reported in the paper and should not be treated as precomputed scores for every subset in this release unless the same split and evaluation protocol are reproduced.

FinixDigital

Model	Overall	TextEdit	TableTEDS	TableTEDS-S	ReadOrderEdit
Qwen3-VL-4B	80.18	0.145	76.04	81.23	0.210
FinixDoc-VL	93.19	0.039	92.07	93.67	0.086
DeepSeek-OCR-2	82.80	0.139	90.00	92.32	0.277
FireRed-OCR	83.10	0.119	87.10	89.14	0.259
PaddleOCR-VL-1.5	85.41	0.116	86.12	88.26	0.183
GLM-OCR	86.28	0.121	89.44	90.99	0.185
Youtu-Parsing	89.26	0.091	87.79	90.85	0.109
Dots.OCR	90.36	0.058	89.78	92.26	0.129
MinerU 2.5	92.96	0.045	91.18	92.70	0.078
Qwen3.5-397B-A17B	84.90	0.119	87.30	89.51	0.207
Kimi-K2.5	85.05	0.119	85.95	88.24	0.189
Qwen3-VL-235B-A22B-Instruct	87.26	0.076	82.77	85.44	0.134

FinixPhoto

Model	Overall	TextEdit	TableTEDS	TableTEDS-S	ReadOrderEdit
Qwen3-VL-4B	54.28	0.408	50.13	63.04	0.465
FinixDoc-VL	67.03	0.276	69.08	77.69	0.404
PaddleOCR-VL-1.5	41.28	0.512	34.54	46.72	0.595
MinerU 2.5	43.08	0.513	35.54	48.58	0.550
DeepSeek-OCR-2	43.20	0.459	30.69	43.52	0.552
GLM-OCR	45.82	0.521	50.47	60.22	0.609
FireRed-OCR	47.20	0.487	38.50	53.72	0.482
Dots.OCR	52.57	0.399	44.90	56.92	0.473
Youtu-Parsing	60.90	0.345	59.01	66.23	0.418
Qwen3.5-397B-A17B	62.58	0.384	62.04	71.82	0.359
Qwen3-VL-235B-A22B-Instruct	62.65	0.359	63.55	72.13	0.397
Kimi-K2.5	65.55	0.325	70.16	77.34	0.410

FinixHuge

Model	Success Rate	Overall	TextEdit	TableTEDS	TableTEDS-S	ReadOrderEdit
FinixDoc	0.92	68.23	0.357	57.09	60.10	0.167
Qwen3-VL-235B-A22B-Instruct	0.68	34.85	0.847	47.05	63.20	0.578
GLM-OCR	0.34	38.06	0.816	59.39	62.43	0.636

Intended Uses

This dataset is intended for:

• Evaluating OCR and document parsing systems on financial-domain documents.
• Testing full-page Markdown reconstruction.
• Testing layout parsing, table parsing, bounding boxes, and reading-order recovery on FinixDigital and FinixPhoto.
• Measuring robustness on noisy camera-captured receipt images.
• Evaluating end-to-end processability on ultra-large document pages.

Out-of-Scope Uses

This dataset is not intended for:

• Individual profiling or personal information extraction.
• Automated financial, medical, insurance, legal, employment, credit, or similarly consequential decision-making.
• Reporting benchmark numbers after using benchmark labels or ground truth for training, fine-tuning, data augmentation, or prompt optimization.
• Claiming complete coverage of all financial document scenarios.

Limitations

FinixDocBench is an evaluation benchmark, not a comprehensive training corpus. This release covers selected high-value financial document parsing scenarios and does not include the private FinixInner track.

FinixPhoto is derived from public-scenario medical receipt sources and re-annotated under the FinixDocBench schema. Prior exposure of some external models to the original public sources cannot be fully ruled out.

FinixHuge emphasizes system-level processability with Markdown-only public annotations. Direct single-pass model comparisons may understate or overstate practical usability if failed pages, truncation, or invalid outputs are not reported consistently.

Some page images may be very large. Users should use image loading libraries carefully and configure decompression or pixel limits intentionally when evaluating FinixHuge.

License

This FinixDocBench release is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

See LICENSE.md for the human-readable license notice and the official Creative Commons license link.

Citation

If you use this FinixDocBench release, please cite:

@misc{wang2026finixdoc,
  title        = {FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks},
  author       = {Hang Wang and Jin Zhang and Guoliang Xu and Pengyue Lu and Yao Li and Zijiao Zhang and Tianyu Huang and Weiqi Xiong and Yulong Wang and Chuqiao Lu and Wenkang Huang and Kai Yang and Yadong Li and Hui Li and Xingzhong Xu and Xiao Xu},
  year         = {2026},
  institution  = {Ant Group},
  url          = {https://finix.alipay.com}
}

Contact

For questions about the benchmark, please contact the FinixDoc authors through the project page or the Ant Group Hugging Face organization.