# Logics-Parsing **Repository Path**: mirrors_alibaba/Logics-Parsing ## Basic Information - **Project Name**: Logics-Parsing - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-12 - **Last Updated**: 2025-09-27 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

🤗 Model   |   🤖 Demo   |   📑 Technical Report

## Introduction
LogicsDocBench 概览
研报示例 化学分子式示例 论文示例 手写示例
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 torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124 ``` ### 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/)