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Data Extraction from Charts via Single Deep Neural Network

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arxiv 1906.11906 v1 pith:Q6WEBVIO submitted 2019-06-06 cs.CV cs.CLcs.LG

Xiaoyi Liu , Diego Klabjan , Patrick NBless This is my paper
classification cs.CVcs.CLcs.LG
keywords chartsdatadeepextractionframeworkmodelnetworkneural
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A JEPA encoder finetuned on synthetic bar charts enables a lightweight decoder to recover bar values from chart images, but the method remains behind state-of-the-art supervised systems.

  2. PaCoNet: Deep Data Extraction for Parallel Coordinates

    cs.CV 2026-08 conditional novelty 6.0 of 10

    PaCoNet combines cropping, hue-based category separation, U-Net restoration, and Hough-based line detection to recover line coordinates from parallel coordinate plots, supported by a new synthetic dataset.

  3. CHAOS: Chart Analysis with Outlier Samples

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A chart perturbation robustness benchmark with five textual and ten visual distortion types, three human-calibrated severity levels, and evaluations of 13 MLLMs on ChartQA and chart summarization.

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