Chart Parsing Model Module Usage Tutorial

I. Overview

Multimodal chart parsing is a cutting-edge technology in the OCR field, focusing on automatically converting various types of visual charts (such as bar charts, line charts, pie charts, etc.) into underlying data tables and formatting the output. Traditional methods rely on complex orchestration of models like chart key point detection, which involves many prior assumptions and lacks robustness. The models in this module utilize the latest VLM technology, driven by data, learning robust features from massive real-world data. Its application scenarios cover financial analysis, academic research, business reports, etc. — for instance, quickly extracting growth trend data from financial statements, experimental comparison values from scientific papers, or user distribution statistics from market research, assisting users in shifting from "viewing charts" to "using data."

II. Supported Model List

Model	Model Download Link	Model parameter size（B）	Model Storage Size (GB)	Model Score	Description
PP-Chart2Table	Inference Model	0.58	1.4	80.60	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 accuracy on par with 7-billion parameter Vision Language Models (VLMs) in critical applications.

Note: The above model scores are the results of internal evaluation set model testing, with a total of 1801 data points, including various chart types such as bar charts, line charts, and pie charts for testing samples under various scenarios such as financial reports, laws and regulations, contracts, etc. There are currently no plans to make them public.

❗ Note：The PP-Chart2Table model was upgraded on June 27, 2025. If you need to use the weights from the previous version of the model, please click the download link

III. Quick Integration

❗ Before quick integration, please install the PaddleX wheel package. For details, please refer to PaddleX Local Installation Tutorial

After completing the installation of the whl package, inference of the document-like visual language model module can be completed with just a few lines of code. You can freely switch models under this module, and you can also integrate model inference from the open document-like visual language model module into your project. Before running the following code, please download the sample image locally.

from paddlex import create_model
model = create_model('PP-Chart2Table')
results = model.predict(
    input={"image": "chart_parsing_02.png"},
    batch_size=1
)
for res in results:
    res.print()
    res.save_to_json(f"./output/res.json")

After running, the result is:

{'res': {'image': 'chart_parsing_02.png', 'result': '年份 | 单家五星级旅游饭店年平均营收 (百万元) | 单家五星级旅游饭店年平均利润 (百万元)\n2018 | 104.22 | 9.87\n2019 | 99.11 | 7.47\n2020 | 57.87 | -3.87\n2021 | 68.99 | -2.9\n2022 | 56.29 | -9.48\n2023 | 87.99 | 5.96'}}

The meanings of the result parameters are as follows: - image: Indicates the path of the input image to be predicted - result: The result information predicted by the model

The visualized printed prediction result is as follows:

年份 | 单家五星级旅游饭店年平均营收 (百万元) | 单家五星级旅游饭店年平均利润 (百万元)
2018 | 104.22 | 9.87
2019 | 99.11 | 7.47
2020 | 57.87 | -3.87
2021 | 68.99 | -2.9
2022 | 56.29 | -9.48
2023 | 87.99 | 5.96

Related methods, parameters, and descriptions are as follows:

• create_model instantiates the document-like visual language model (taking PP-Chart2Table as an example here), with specific explanations as follows:

Parameter	Description	Type	Options	Default
model_name	Model name	str	None	None
model_dir	Model storage path	str	None	None
device	Model inference device	str	Support specifying specific GPU card number, such as "gpu:0", other hardware specific card numbers, such as "npu:0", CPU as "cpu".	gpu:0
use_hpip	Whether to enable high-performance inference plugins. Currently not supported.	bool	None	False
hpi_config	High-performance inference configuration. Currently not supported.	dict | None	None	None

• Among them, model_name must be specified. After specifying model_name, the default PaddleX built-in model parameters are used. On this basis, if model_dir is specified, the user-defined model is used.

• Call the predict() method of the document-like visual language model for inference prediction. The predict() method parameters include input, batch_size, with specific explanations as follows:

Parameter	Description	Type	Options	Default
input	Data to be predicted	dict	Dict, as multimodal models have different input requirements, it needs to be determined based on the specific model. Specifically: The input form for PP-Chart2Table is {'image': image_path}	None
batch_size	Batch size	int	Integer	1

• Process the prediction results. The prediction result for each sample is the corresponding Result object, which supports operations like printing and saving as a json file:

Method	Description	Parameter	Type	Description	Default
print()	Print results to terminal	format_json	bool	Whether to format the output content using JSON indentation	True
		indent	int	Specify the indentation level to beautify the output JSON data for better readability, only effective when format_json is True	4
		ensure_ascii	bool	Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False will keep the original characters, only effective when format_json is True	False
save_to_json()	Save the result as a json formatted file	save_path	str	Path to save the file. When it's a directory, the saved file name matches the input file type name	None
		indent	int	Specify the indentation level to beautify the output JSON data for better readability, only effective when format_json is True	4
		ensure_ascii	bool	Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False will keep the original characters, only effective when format_json is True	False

• Additionally, it is also possible to obtain prediction results through attributes, as follows:

Attribute	Description
json	Get the prediction result in json format

For more information on using the API for single model inference in PaddleX, you can refer to PaddleX Single Model Python Script Usage Instructions.

IV. Secondary Development

The current module temporarily does not support fine-tuning training, only inference integration. Support for fine-tuning training in this module is planned for the future.

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