---
license: cdla-permissive-2.0
task_categories:
- image-text-to-text
tags:
- ocr
- chart
pretty_name: SynthChartNet
size_categories:
- 1M<n<10M
---
# SynthChartNet

<div style="display: flex; justify-content: center; align-items: center;">
    <img src="https://cdn-uploads.huggingface.co/production/uploads/663e1254887b6f5645a0399f/Kgt6S5S_zPGGQ3IlmyRVB.png" alt="Chart Example" style="width: 800px; height: auto">
</div>

**SynthChartNet** is a multimodal dataset designed for training the **SmolDocling** model on chart-based document understanding tasks. It consists of **1,981,157** synthetically generated samples, where each image depicts a chart (e.g., line chart, bar chart, pie chart, stacked bar chart), and the associated ground truth is given in **OTSL** format.

Charts were rendered at 120 DPI using a diverse set of visualization libraries: **Matplotlib**, **Seaborn**, and **Pyecharts**, enabling visual variability in layout, style, and color schemes.

---

## Dataset Statistics

* **Total samples**: 1,981,157

  * **Training set**: 1,981,157

* **Modalities**: Image, Text (OTSL format)

* **Chart Types**: Line, Bar, Pie, Stacked Bar

* **Rendering Engines**: Matplotlib, Seaborn, Pyecharts

---

## Data Format

Each dataset entry is structured as follows:

```json
{
  "images": [PIL Image],
  "texts": [
    {
      "assistant": "<loc_x0><loc_y0><loc_x1><loc_y1><_Chart_>OTSL_REPRESENTATION</chart>",
      "source": "SynthChartNet",
      "user": "<chart>"
    }
  ]
}
```

---

## Intended Use

* Training multimodal models for **chart understanding**, specifically:

  * Chart parsing and transcription to structured formats (OTSL)

---

## Citation

If you use SynthChartNet, please cite:

```bibtex
@article{nassar2025smoldocling,
  title={SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion},
  author={Nassar, Ahmed and Marafioti, Andres and Omenetti, Matteo and Lysak, Maksym and Livathinos, Nikolaos and Auer, Christoph and Morin, Lucas and de Lima, Rafael Teixeira and Kim, Yusik and Gurbuz, A Said and others},
  journal={arXiv preprint arXiv:2503.11576},
  year={2025}
}
@inproceedings{lysak2023optimized,
  title={Optimized table tokenization for table structure recognition},
  author={Lysak, Maksym and Nassar, Ahmed and Livathinos, Nikolaos and Auer, Christoph and Staar, Peter},
  booktitle={International Conference on Document Analysis and Recognition},
  pages={37--50},
  year={2023},
  organization={Springer}
}
```