--- title: PP-Chart2Table canonical_url: "https://www.modelscope.cn/models/PaddlePaddle/PP-Chart2Table" md_url: "https://www.modelscope.cn/models/PaddlePaddle/PP-Chart2Table.md" repository: PaddlePaddle/PP-Chart2Table last_updated: 2026-06-12 license: apache-2.0 model_type: - GOT architectures: - GOTQwenForCausalLM language: - en - zh downloads: 4002 stars: 3 tags: - OCR - PaddlePaddle - PaddleOCR - chart_parsing --- # PP-Chart2Table > PP-Chart2Table - PaddlePaddle 在 ModelScope 开源的模型。PP-Chart2Table is a SOTA multimodal model developed by the PaddlePaddle team, specializing in chart parsing for both Chinese and English. Its high performance is driven by a novel "Shuffled Chart Data… PaddlePaddle/PP-Chart2Table 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。 - **Repository**: PaddlePaddle/PP-Chart2Table - **License**: apache-2.0 - **Tags**: OCR, PaddlePaddle, PaddleOCR, chart_parsing - **Downloads**: 4002 - **Stars**: 3 - **Last updated**: 2026-06-12 Source: https://www.modelscope.cn/models/PaddlePaddle/PP-Chart2Table --- # PP-Chart2Table ## Introduction PP-Chart2Table is a SOTA multimodal model developed by the PaddlePaddle team, specializing in chart parsing for both Chinese and English. Its high performance is driven by a novel "Shuffled Chart Data Retrieval" training task, which, combined with a refined token masking strategy, significantly improves its efficiency in converting charts to data tables. The model is further strengthened by an advanced data synthesis pipeline that uses high-quality seed data, RAG, and LLMs persona design to create a richer, more diverse training set. To address the challenge of large-scale unlabeled, out-of-distribution (OOD) data, the team implemented a two-stage distillation process, ensuring robust adaptability and generalization on real-world data. In-house benchmarks demonstrate that PP-Chart2Table not only outperforms models of a similar scale but also achieves performance on par with 7-billion parameter Vision Language Models (VLMs) in critical application scenarios. ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleX Install the latest version of the PaddleX inference package from PyPI: ```bash python -m pip install paddlex && python -m pip install "paddlex[multimodal]" ``` ### Model Usage You can integrate the model inference of PP-Chart2Table into your project. Before running the following code, please download the sample image to your local machine. ```python from paddlex import create_model model = create_model('PP-Chart2Table') results = model.predict( input={"image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/OrlFuIXQUhO3Fg1G9_H1u.png"}, batch_size=1 ) for res in results: res.print() res.save_to_json(f"./output/res.json") ``` After running, the obtained result is as follows: ```bash {'res': {'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/OrlFuIXQUhO3Fg1G9_H1u.png', 'result': 'Agency | Favorable | Not Sure | Unfavorable\nNational Park Service | 81% | 12% | 7%\nU.S. Postal Service | 77% | 3% | 20%\nNASA | 74% | 17% | 9%\nSocial Security Administration | 61% | 12% | 28%\nCDC | 56% | 6% | 38%\nVeterans Affairs | 56% | 16% | 28%\nEPA | 55% | 14% | 31%\nHealth and Human Services | 55% | 15% | 30%\nFBI | 52% | 12% | 36%\nDepartment of Transportation | 52% | 12% | 36%\nDepartment of Homeland Security | 51% | 18% | 35%\nDepartment of Justice | 49% | 10% | 41%\nCIA | 46% | 21% | 33%\nDepartment of Education | 45% | 8% | 47%\nFederal Reserve | 43% | 20% | 37%\nIRS | 42% | 7% | 51%'}} ``` The visualized result is as follows: For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/vlm_modules/chart_parsing.html#iii-quick-integration). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios. #### PP-StructureV3 Layout analysis is a technique used to extract structured information from document images. PP-StructureV3 includes the following seven modules: * Layout Detection Module * Chart Recognition Module（Optional） * General OCR Sub-pipeline * Document Image Preprocessing Sub-pipeline （Optional） * Table Recognition Sub-pipeline （Optional） * Seal Recognition Sub-pipeline （Optional） * Formula Recognition Sub-pipeline （Optional） You can quickly experience the PP-StructureV3 pipeline with a single command. ```bash paddleocr pp_structurev3 --chart_recognition_model_name PP-Chart2Table \ --use_chart_recognition True \ -i https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/Mk1PKgszCEEutZukT3FPB.png ``` You can experience the inference of the pipeline with just a few lines of code. Taking the PP-StructureV3 pipeline as an example: ```python from paddleocr import PPStructureV3 pipeline = PPStructureV3(chart_recognition_model_name="PP-Chart2Table", use_chart_recognition=True) # ocr = PPStructureV3(use_doc_orientation_classify=True) # Use use_doc_orientation_classify to enable/disable document orientation classification model # ocr = PPStructureV3(use_doc_unwarping=True) # Use use_doc_unwarping to enable/disable document unwarping module # ocr = PPStructureV3(use_textline_orientation=True) # Use use_textline_orientation to enable/disable textline orientation classification model # ocr = PPStructureV3(device="gpu") # Use device to specify GPU for model inference output = pipeline.predict("./Mk1PKgszCEEutZukT3FPB.png", use_chart_recognition=True) for res in output: res.print() ## Print the structured prediction output res.save_to_json(save_path="output") ## Save the current image's structured result in JSON format res.save_to_markdown(save_path="output") ## Save the current image's result in Markdown format ``` The default model used in pipeline is `PP-Chart2Table`, so you don't have to specify `PP-Chart2Table` for the `chart_recognition_model_name argument`, but you can use the local model file by argument `chart_recognition_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/PP-StructureV3.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) [PaddleX Repo](https://github.com/paddlepaddle/paddlex) [PaddleX Documentation](https://paddlepaddle.github.io/PaddleX/latest/en/index.html)