---
title: Logics-Parsing-v2
canonical_url: "https://www.modelscope.ai/models/Alibaba-DT/Logics-Parsing-v2"
md_url: "https://www.modelscope.ai/models/Alibaba-DT/Logics-Parsing-v2.md"
repository: Alibaba-DT/Logics-Parsing-v2
last_updated: 2026-02-15
model_type:
- qwen3_vl
architectures:
- Qwen3VLForConditionalGeneration
parameters: 4.4B
tensor_type:
- BF16
library_name:
- safetensors
- pytorch
frameworks:
- pytorch
downloads: 146
stars: 0
---
# Logics-Parsing-v2
> Logics-Parsing-v2 - An open-source model by Alibaba-DT on ModelScope. ๐ป HomePage | ๐ค GitHub | ๐ค Demo
- **Repository**: Alibaba-DT/Logics-Parsing-v2
- **Parameters**: 4.4B
- **Downloads**: 146
- **Stars**: 0
- **Last updated**: 2026-02-15
Source: https://www.modelscope.ai/models/Alibaba-DT/Logics-Parsing-v2
---
๐ป HomePage | ๐ค GitHub | ๐ค Demo
## Updates
* [2026/02/13] ๐๐๐๐๐ We release Logics-Parsing-v2 Model.
* [2025/09/25] ๐๐๐We release Logics-Parsing Model.
## Introduction
**Logics-Parsing-v2** is an advanced evolution of the previously proposed Logics-Parsing (v1). It inherits all the core capabilities of v1 model, while demonstrating more powerful capabilities on handling complex documents. Furthermore, it extends support for **Parsing-2.0** scenarios, enabling structured parsing of musical sheets, flowcharts, as well as code/pseudocode blocks.
## Key Features
* **Effortless End-to-End Processing**
* End-to-end recognition and parsing for various kinds of document elements within a single model.
* Handles complex-layout and text-dense documents such as newspapers and magazines with exceptional precision and ease;
* **Advanced Content Recognition**
* Smaller in size, greater in performance, delivering more accurate and structured parsing of tables and scientific formulas.
* Introducing **Parsing-2.0**: natively supports parsing of diverse structured content, including flowcharts, music sheets and pseudocode blocks.
* **Rich, Structured HTML Output**
* Transforms documents into concise HTML -- capturing not just content, but also element types, spatial layouts, and semantic hierarchy.
* More scientific and intuitive formats for structured elements -- such as Mermaid for flowcharts and ABC notation for musical scores.
* **State-of-the-Art Performance**
* SOTA across the board: Logics-Parsing-v2 sets top records on both our in-house benchmark (overall score: **82.16**) and the renowned public benchmark OmniDocBench-v1.5 (overall score: **93.23**).
## Benchmark
### Comparisons on LogicsDocBench
We introduce **LogicsDocBench**, a new comprehensive evaluation benchmark comprising 900 carefully selected PDF pages, covering both traditional document Parsing-1.0 tasks and the newly introduced Parsing-2.0 scenarios. This benchmark is designed to better assess modelsโ capabilities in complex and diverse real-world documents parsing. The dataset is organized into three core document subsets:
* **STEM Documents (218 pages)**:
Focuses on high-difficulty academic and educational content, spanning over ten domains including physics, mathematics, engineering, and interdisciplinary sciences. This subset evaluates deep understanding of mathematical formulas, technical terminology, and structured knowledge representation.
* **Complex Layouts (459 pages)**:
Includes challenging real-world layouts such as multi-column text, cross-page tables, vertical writing, and mixed text-image arrangements. This subset comprehensively evaluate a modelโs layout analysis abilities.
* **Parsing-2.0 Content (223 pages)**:
Targets modern digital and semi-structured content that poses significant challenges for traditional OCR systems, including:
* Chemical Molecular formulas
* Musical sheets
* Code and pseudo-code block
* Flowcharts and mind maps
For Parsing-1.0 tasks, we adopt the same evaluation protocols as OmniDocBench-v1.5 to ensure fairness and consistency across benchmarks. For Parsing-2.0, we report fine-grained results using edit distance for each subcategory, and compute an overall score as follows:
$$\small \text{Overall} = \frac{Parsing1.0^{Overall} \times 3 + (1-{Chemistry}^{Edit})\times 100 + (1-{Code}^{Edit})\times 100 + (1-{Chart}^{Edit})\times 100 + (1-{Music}^{Edit})\times 100}{7}$$
Comprehensive evaluation of document parsing on LogicsDocBench is listed as follows:
The histogram below provides a more intuitive visualization of the advantages of our Logics-Parsing-v2 model in both Parsing-1.0 and 2.0 scenarios.
### Comparisons on OmniDocBench_v1.5
We also provide the experimental results of our newly proposed Logics-Parsing-v2 model on the widely recognized open-source benchmark OmniDocBench-v1.5. As shown in the table below, Logics-Parsing-v2 achieves the highest scores among all other approaches, demonstrating its effectiveness and superiority.
_\* The model results in the table are sourced from the official OmniDocBench website._
## Quick Start
### 1. Installation
```shell
conda create -n logis-parsing-v2 python=3.10
conda activate logis-parsing-v2
pip install -r requirements.txt
```
### 2. Download Model Weights
```
# Download our model from Modelscope.
pip install modelscope
python download_model_v2.py -t modelscope
# Download our model from huggingface.
pip install huggingface_hub
python download_model_v2.py -t huggingface
```
### 3. Inference
```shell
python3 inference_v2.py --image_path PATH_TO_INPUT_IMG --output_path PATH_TO_OUTPUT --model_path PATH_TO_MODEL
```
## Showcases
## Acknowledgments
We would like to acknowledge the following open-source projects that provided inspiration and reference for this work:
- [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL)
- [OmniDocBench](https://github.com/opendatalab/OmniDocBench)
- [Mathpix](https://mathpix.com/)
