https://github.com/alibaba/Logics-Parsing
https://github.com/alibaba/Logics-Parsing
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/alibaba/Logics-Parsing
- Owner: alibaba
- License: apache-2.0
- Created: 2025-09-11T02:57:24.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-30T06:59:08.000Z (8 months ago)
- Last Synced: 2025-09-30T08:41:00.054Z (8 months ago)
- Language: Python
- Size: 33.9 MB
- Stars: 266
- Watchers: 3
- Forks: 18
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-LLM-resources - Logics-Parsing
- StarryDivineSky - alibaba/Logics-Parsing - Parsing 是由阿里巴巴开源的高性能端到端文档解析模型，基于视觉语言模型（VLM）通过监督微调（SFT）和强化学习（RL）训练而成。核心特点包括： 1. 端到端处理能力 单模型直接处理文档图像，无需复杂流水线，支持复杂布局文档解析。 2. 多模态内容识别 精准识别科学公式、化学结构（可转SMILES格式），并过滤页眉页脚等冗余信息。 3. 结构化输出 生成带分类标签、坐标和OCR文本的HTML，保留文档逻辑结构。 4. 性能领先 在自建评测集LogicsDocBench（1078页复杂文档）上全面超越主流方案（如Mathpix、Gemini等），尤其在公式识别（Edit↓ 0.106）和表格处理（TEDS↑ 79.5）表现突出。 5. 便捷部署 支持Modelscope/Hugging Face模型下载，Python一键推理。 开源协议：Apache-2.0，适用于科研文档、化学材料等复杂场景解析。 (光学字符识别OCR / 资源传输下载)
README
🤗 Model   |   🤖 Demo   |   📑 Technical Report
## Introduction
report
chemistry
paper
handwritten
Logics-Parsing is a powerful, end-to-end document parsing model built upon a general Vision-Language Model (VLM) through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). It excels at accurately analyzing and structuring highly complex documents.
## Key Features
* **Effortless End-to-End Processing**
* Our single-model architecture eliminates the need for complex, multi-stage pipelines. Deployment and inference are straightforward, going directly from a document image to structured output.
* It demonstrates exceptional performance on documents with challenging layouts.
* **Advanced Content Recognition**
* It accurately recognizes and structures difficult content, including intricate scientific formulas.
* Chemical structures are intelligently identified and can be represented in the standard **SMILES** format.
* **Rich, Structured HTML Output**
* The model generates a clean HTML representation of the document, preserving its logical structure.
* Each content block (e.g., paragraph, table, figure, formula) is tagged with its **category**, **bounding box coordinates**, and **OCR text**.
* It automatically identifies and filters out irrelevant elements like headers and footers, focusing only on the core content.
* **State-of-the-Art Performance**
* Logics-Parsing achieves the best performance on our in-house benchmark, which is specifically designed to comprehensively evaluate a model’s parsing capability on complex-layout documents and STEM content.
## Benchmark
Existing document-parsing benchmarks often provide limited coverage of complex layouts and STEM content. To address this, we constructed an in-house benchmark comprising 1,078 page-level images across nine major categories and over twenty sub-categories. Our model achieves the best performance on this benchmark.
Model Type
Methods
Overall Edit ↓
Text Edit Edit ↓
Formula Edit ↓
Table TEDS ↑
Table Edit ↓
ReadOrderEdit ↓
ChemistryEdit ↓
HandWritingEdit ↓
EN
ZH
EN
ZH
EN
ZH
EN
ZH
EN
ZH
EN
ZH
ALL
ALL
Pipeline Tools
doc2x
0.209
0.188
0.128
0.194
0.377
0.321
81.1
85.3
0.148
0.115
0.146
0.122
1.0
0.307
Textin
0.153
0.158
0.132
0.190
0.185
0.223
76.7
86.3
0.176
0.113
0.118
0.104
1.0
0.344
mathpix*
0.128
0.146
0.128
0.152
0.06
0.142
86.2
86.6
0.120
0.127
0.204
0.164
0.552
0.263
PP_StructureV3
0.220
0.226
0.172
0.29
0.272
0.276
66
71.5
0.237
0.193
0.201
0.143
1.0
0.382
Mineru2
0.212
0.245
0.134
0.195
0.280
0.407
67.5
71.8
0.228
0.203
0.205
0.177
1.0
0.387
Marker
0.324
0.409
0.188
0.289
0.285
0.383
65.5
50.4
0.593
0.702
0.23
0.262
1.0
0.50
Pix2text
0.447
0.547
0.485
0.577
0.312
0.465
64.7
63.0
0.566
0.613
0.424
0.534
1.0
0.95
Expert VLMs
Dolphin
0.208
0.256
0.149
0.189
0.334
0.346
72.9
60.1
0.192
0.35
0.160
0.139
0.984
0.433
dots.ocr
0.186
0.198
0.115
0.169
0.291
0.358
79.5
82.5
0.172
0.141
0.165
0.123
1.0
0.255
MonkeyOcr
0.193
0.259
0.127
0.236
0.262
0.325
78.4
74.7
0.186
0.294
0.197
0.180
1.0
0.623
OCRFlux
0.252
0.254
0.134
0.195
0.326
0.405
58.3
70.2
0.358
0.260
0.191
0.156
1.0
0.284
Gotocr
0.247
0.249
0.181
0.213
0.231
0.318
59.5
74.7
0.38
0.299
0.195
0.164
0.969
0.446
Olmocr
0.341
0.382
0.125
0.205
0.719
0.766
57.1
56.6
0.327
0.389
0.191
0.169
1.0
0.294
SmolDocling
0.657
0.895
0.486
0.932
0.859
0.972
18.5
1.5
0.86
0.98
0.413
0.695
1.0
0.927
Logics-Parsing
0.124
0.145
0.089
0.139
0.106
0.165
76.6
79.5
0.165
0.166
0.136
0.113
0.519
0.252
General VLMs
Qwen2VL-72B
0.298
0.342
0.142
0.244
0.431
0.363
64.2
55.5
0.425
0.581
0.193
0.182
0.792
0.359
Qwen2.5VL-72B
0.233
0.263
0.162
0.24
0.251
0.257
69.6
67
0.313
0.353
0.205
0.204
0.597
0.349
Doubao-1.6
0.188
0.248
0.129
0.219
0.273
0.336
74.9
69.7
0.180
0.288
0.171
0.148
0.601
0.317
GPT-5
0.242
0.373
0.119
0.36
0.398
0.456
67.9
55.8
0.26
0.397
0.191
0.28
0.88
0.46
Gemini2.5 pro
0.185
0.20
0.115
0.155
0.288
0.326
82.6
80.3
0.154
0.182
0.181
0.136
0.535
0.26
* Tested on the v3/PDF Conversion API (August 2025 deployment).
## Quick Start
### 1. Installation
```shell
conda create -n logis-parsing python=3.10
conda activate logis-parsing
pip install -r requirement.txt
```
### 2. Download Model Weights
```
# Download our model from Modelscope.
pip install modelscope
python download_model.py -t modelscope
# Download our model from huggingface.
pip install huggingface_hub
python download_model.py -t huggingface
```
### 3. Inference
```shell
python3 inference.py --image_path PATH_TO_INPUT_IMG --output_path PATH_TO_OUTPUT --model_path PATH_TO_MODEL
```
## Acknowledgments
We would like to acknowledge the following open-source projects that provided inspiration and reference for this work:
- [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL)
- [OmniDocBench](https://github.com/opendatalab/OmniDocBench)
- [Mathpix](https://mathpix.com/)