# Data Extraction from Charts via Single Deep Neural Network

arXiv: https://arxiv.org/abs/1906.11906 · HTML: https://mlsanity.com/paper/1906.11906/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 24 of 24)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [OneChart: Purify the Chart Structural Extraction via One Auxiliary Token](https://mlsanity.com/paper/2404.09987/) (2024)
- [Chart Question Answering: State of the Art and Future Directions](https://mlsanity.com/paper/2205.03966/) (2022)
- [ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules](https://mlsanity.com/paper/2304.02173/) (2023)
- [AutoChart: A Dataset for Chart-to-Text Generation Task](https://mlsanity.com/paper/2108.06897/) (2021)
- [Towards an efficient framework for Data Extraction from Chart Images](https://mlsanity.com/paper/2105.02039/) (2021)
- [Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization](https://mlsanity.com/paper/2312.10610/) (2023)
- [ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition](https://mlsanity.com/paper/2308.07743/) (2023)
- [Leveraging Vision Capabilities of Multimodal LLMs for Automated Data Extraction from Plots](https://mlsanity.com/paper/2503.12326/) (2025)
- [InvVis: Large-Scale Data Embedding for Invertible Visualization](https://mlsanity.com/paper/2307.16176/) (2023)
- [Line Graphics Digitization: A Step Towards Full Automation](https://mlsanity.com/paper/2307.02065/) (2023)
- [Advancing Chart Question Answering with Robust Chart Component Recognition](https://mlsanity.com/paper/2407.21038/) (2024)
- [ChartEye: A Deep Learning Framework for Chart Information Extraction](https://mlsanity.com/paper/2408.16123/) (2024)
- [CHARTER: heatmap-based multi-type chart data extraction](https://mlsanity.com/paper/2111.14103/) (2021)
- [VizExtract: Automatic Relation Extraction from Data Visualizations](https://mlsanity.com/paper/2112.03485/) (2021)
- [A Survey and Approach to Chart Classification](https://mlsanity.com/paper/2307.04147/) (2023)
- [A Survey on Figure Classification Techniques in Scientific Documents](https://mlsanity.com/paper/2307.05694/) (2023)
- [GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering](https://mlsanity.com/paper/2409.02611/) (2024)
- [An extensible point-based method for data chart value detection](https://mlsanity.com/paper/2308.11788/) (2023)
- [CHAOS: Chart Analysis with Outlier Samples](https://mlsanity.com/paper/2505.17235/) (2025)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)
- [Semantic Segmentation of Node and Edge Diagrams for Assistive Technology](https://mlsanity.com/paper/2606.11320/) (2026)
- [PaCoNet: Deep Data Extraction for Parallel Coordinates](https://mlsanity.com/paper/2608.06030/) (2026)
- [Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2608.06062/) (2026)

## Abstract (arXiv)

Automatic data extraction from charts is challenging for two reasons: there exist many relations among objects in a chart, which is not a common consideration in general computer vision problems; and different types of charts may not be processed by the same model. To address these problems, we propose a framework of a single deep neural network, which consists of object detection, text recognition and object matching modules. The framework handles both bar and pie charts, and it may also be extended to other types of charts by slight revisions and by augmenting the training data. Our model performs successfully on 79.4% of test simulated bar charts and 88.0% of test simulated pie charts, while for charts outside of the training domain it degrades for 57.5% and 62.3%, respectively.