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Data Extraction from Charts via Single Deep Neural Network

arXiv 1906.11906 ↗First posted 6 Jun 2019cs.CV cs.CL cs.LGcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 2; 2023: 8; 2024: 4; 2025: 2; 2026: 4.

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  1. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  2. 2024OneChart: Purify the Chart Structural Extraction via One Auxiliary Tokencited by 39
  3. 2022Chart Question Answering: State of the Art and Future Directionscited by 26
  4. 2023ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rulescited by 24
  5. 2021AutoChart: A Dataset for Chart-to-Text Generation Taskcited by 12
  6. 2021Towards an efficient framework for Data Extraction from Chart Imagescited by 10
  7. 2023Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarizationcited by 10
  8. 2023ChartDETR: A Multi-shape Detection Network for Visual Chart Recognitioncited by 7
  9. 2025Leveraging Vision Capabilities of Multimodal LLMs for Automated Data Extraction from Plotscited by 7
  10. 2023InvVis: Large-Scale Data Embedding for Invertible Visualizationcited by 6
  11. 2023Line Graphics Digitization: A Step Towards Full Automationcited by 5
  12. 2024Advancing Chart Question Answering with Robust Chart Component Recognitioncited by 5
12 more
  1. 2024ChartEye: A Deep Learning Framework for Chart Information Extractioncited by 4
  2. 2021CHARTER: heatmap-based multi-type chart data extractioncited by 2
  3. 2021VizExtract: Automatic Relation Extraction from Data Visualizationscited by 2
  4. 2023A Survey and Approach to Chart Classificationcited by 2
  5. 2023A Survey on Figure Classification Techniques in Scientific Documentscited by 2
  6. 2024GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answeringcited by 2
  7. 2023An extensible point-based method for data chart value detectioncited by 1
  8. 2025CHAOS: Chart Analysis with Outlier Samplescited by 1
  9. 2026Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancementcited by 1
  10. 2026Semantic Segmentation of Node and Edge Diagrams for Assistive Technologycited by 0
  11. 2026PaCoNet: Deep Data Extraction for Parallel Coordinatescited by 0
  12. 2026Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecturecited by 0