tencent/WeVisDoc-2B

WeVisDoc

[English](README.md) | [简体中文](README.zh-CN.md)

WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables.

WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings.

WeVisDoc-4B leads the compared end-to-end parsers across all four reported settings. Bars show scores on OmniDocBench v1.6 and PureDocBench Clean, Digital, and Real.

Evaluation

The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs.

OmniDocBench v1.6

| Model | Params | Overall ↑ | TextEdit ↓ | FormulaCDM ↑ | TableTEDS ↑ | TableTEDS_S ↑ | ROEdit ↓ | | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | | Nanonets-OCR2* | 3B | 83.20 | 0.108 | 80.35 | 80.10 | 85.26 | 0.211 | | OCRFlux-3B* | 3B | 83.31 | 0.126 | 88.75 | 73.78 | 77.98 | 0.217 | | POINTS-Reader | 3B | 83.37 | 0.096 | 85.72 | 73.98 | 77.40 | 0.198 | | Nanonets-OCR-s | 3B | 83.61 | 0.108 | 81.46 | 80.18 | 84.51 | 0.213 | | olmOCR-2-7B* | 7B | 85.51 | 0.106 | 88.84 | 78.32 | 82.81 | 0.223 | | olmOCR | 7B | 85.74 | 0.139 | 88.10 | 83.00 | 87.17 | 0.216 | | DeepSeek-OCR* | 3B | 86.31 | 0.077 | 84.71 | 81.87 | 86.07 | 0.171 | | OCRVerse | 4B | 88.60 | 0.063 | 89.61 | 82.44 | 86.27 | 0.163 | | UniRec-0.1B* | 0.1B | 88.91 | 0.088 | 92.14 | 83.40 | 86.79 | 0.146 | | DeepSeek-OCR 2 | 3B | 90.25 | 0.050 | 91.84 | 83.89 | 87.75 | 0.144 | | dots.ocr | 3B | 90.77 | 0.048 | 89.95 | 87.18 | 90.58 | 0.138 | | FD-RL* | 4B | 91.21 | 0.055 | 92.92 | 86.22 | 90.92 | 0.145 | | HunyuanOCR | 1B | 92.03 | 0.048 | 88.60 | 92.37 | 93.99 | 0.138 | | dots.mocr* | 3B | 92.57 | 0.042 | 92.09 | 89.78 | 92.92 | 0.133 | | FireRed-OCR | 2B | 93.26 | 0.037 | 95.44 | 88.04 | 91.06 | 0.131 | | Logics-Parsing-v2 | 4B | 93.33 | 0.041 | 95.65 | 88.42 | 91.98 | 0.137 | | Qianfan-OCR | 4B | 93.90 | 0.040 | 95.08 | 90.53 | 93.31 | 0.130 | | Unlimited-OCR | 3B-A0.5B | 93.92 | 0.042 | 95.79 | 90.16 | 93.32 | 0.129 | | HunyuanOCR-1.5 | 1B | 94.74 | 0.039 | 94.50 | 93.67 | 94.71 | 0.129 | | WeVisDoc-2B | 2B | 95.06 | 0.038 | 95.94 | 93.03 | 95.26 | 0.130 | | WeVisDoc-4B | 4B | 95.38 | 0.036 | 96.81 | 92.95 | 95.34 | 0.125 |

PureDocBench

| Model | Params | Avg₃ ↑ | Clean Overall ↑ | Digital Degraded Overall ↑ | Real Degraded Overall ↑ | | --- | ---: | ---: | ---: | ---: | ---: | | OCRFlux-3B | 3B | 42.06 | 47.14 | 41.82 | 37.21 | | DeepSeek-OCR | 3B | 46.98 | 53.50 | 46.95 | 40.48 | | UniRec-0.1B | 0.1B | 48.59 | 58.91 | 52.42 | 34.44 | | POINTS-Reader* | 3B | 49.24 | 53.78 | 51.24 | 42.69 | | DeepSeek-OCR-2 | 3B | 49.51 | 55.53 | 49.41 | 43.60 | | Qianfan-OCR | 4B | 51.04 | 57.22 | 50.85 | 45.06 | | olmOCR-7B | 7B | 55.90 | 62.56 | 57.84 | 47.30 | | Nanonets-OCR2 | 3B | 58.36 | 64.83 | 61.23 | 49.03 | | HunyuanOCR | 1B | 60.56 | 65.61 | 61.49 | 54.58 | | Unlimited-OCR* | 3B-A0.5B | 62.76 | 71.28 | 63.62 | 53.39 | | olmOCR-2-7B | 7B | 63.78 | 69.36 | 65.87 | 56.10 | | dots.ocr | 3B | 64.55 | 72.01 | 65.95 | 55.68 | | Nanonets-OCR-s* | 3B | 65.37 | 71.26 | 66.56 | 58.28 | | FireRed-OCR | 2B | 65.57 | 70.81 | 68.49 | 57.42 | | HunyuanOCR-1.5* | 1B | 68.79 | 73.98 | 70.81 | 61.59 | | OCRVerse | 4B | 69.40 | 73.18 | 71.36 | 63.66 | | dots.mocr | 3B | 70.39 | 76.27 | 73.16 | 61.73 | | Logics-Parsing-v2 | 4B | 72.61 | 76.35 | 73.85 | 67.64 | | FD-RL | 4B | 73.92 | 78.38 | 76.33 | 67.04 | | WeVisDoc-2B | 2B | 73.86 | 79.36 | 76.62 | 65.60 | | WeVisDoc-4B | 4B | 75.54 | 79.81 | 77.74 | 69.08 |

Avg₃ is the mean of the three PureDocBench track-level Overall scores. * marks baseline results obtained with our evaluation pipeline; unmarked baseline results are taken from the corresponding papers.

Quick start

Python 3.10+ is required. Install the vLLM and client dependencies:

python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements-vllm.txt

Start the service in the first terminal:

bash scripts/serve_vllm.sh Tencent/WeVisDoc-2B

Use Tencent/WeVisDoc-4B instead to run the 4B version.

Then process all bundled images from a second terminal:

source .venv/bin/activate
bash scripts/run_demo.sh

Predictions are written to outputs/predictions/.

Serve with vLLM

python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements-vllm.txt
bash scripts/serve_vllm.sh Tencent/WeVisDoc-2B

Replace the model ID with Tencent/WeVisDoc-4B to serve the 4B version. The launcher requires vLLM >=0.11.1. Extra arguments are passed to vLLM. To expose the service on the network, set HOST=0.0.0.0. For two GPUs and a larger context:

CUDA_VISIBLE_DEVICES=0,1 TENSOR_PARALLEL_SIZE=2 MAX_MODEL_LEN=65536 \
bash scripts/serve_vllm.sh Tencent/WeVisDoc-4B --dtype bfloat16
curl --fail http://127.0.0.1:8000/health

| Environment variable | Default | Meaning | | --- | --- | --- | | WEVISDOC_MODEL_PATH / MODEL_PATH | Unset | Model ID or checkpoint; positional argument takes precedence, then WEVISDOC_MODEL_PATH | | SERVED_MODEL_NAME | wevisdoc | API model alias; also read by the client | | HOST / PORT | 127.0.0.1 / 8000 | Listening address | | TENSOR_PARALLEL_SIZE | 1 | Number of tensor-parallel GPUs | | MAX_MODEL_LEN | 32768 | Total context budget: text, image and output tokens | | GPU_MEMORY_UTILIZATION | 0.9 | GPU memory fraction | | MAX_NUM_SEQS | 8 | Maximum concurrent sequences | | OMP_NUM_THREADS | 1 | CPU preprocessing threads | | VLLM_API_KEY | Unset | Optional server authentication, handled by vLLM |

Use a separate virtual environment from Transformers to avoid conflicting PyTorch packages.

Call the service

A client machine only needs python -m pip install -r requirements.txt. The examples expect PNG, JPEG, or WebP page images:

python -m wevisdoc.client --image page.png --output results/page.md
python -m wevisdoc.client --image-dir images --result-dir results --workers 4

--image-dir processes images in that directory (not recursively) and...