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PaddlePaddle/
PP-Chart2Table

Name: PP-Chart2Table
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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

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.

PaddleX

Install the latest version of the PaddleX inference package from PyPI:

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:

{'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.

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.

Links

PaddleOCR Repo

PaddleOCR Documentation

PaddleX Repo

PaddleX Documentation

PaddlePaddle/PP-Chart2Table