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Computer Science > Computer Vision and Pattern Recognition

arXiv:2408.16123 (cs)
[Submitted on 28 Aug 2024]

Title:ChartEye: A Deep Learning Framework for Chart Information
Extraction

Authors:Osama Mustafa, Muhammad Khizer Ali, Momina Moetesum, Imran
Siddiqi
View a PDF of the paper titled ChartEye: A Deep Learning Framework
for Chart Information Extraction, by Osama Mustafa and 3 other
authors
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    Abstract:The widespread use of charts and infographics as a means
    of data visualization in various domains has inspired recent
    research in automated chart understanding. However, information
    extraction from chart images is a complex multitasked process due
    to style variations and, as a consequence, it is challenging to
    design an end-to-end system. In this study, we propose a deep
    learning-based framework that provides a solution for key steps
    in the chart information extraction pipeline. The proposed
    framework utilizes hierarchal vision transformers for the tasks
    of chart-type and text-role classification, while YOLOv7 for text
    detection. The detected text is then enhanced using Super
    Resolution Generative Adversarial Networks to improve the
    recognition output of the OCR. Experimental results on a
    benchmark dataset show that our proposed framework achieves
    excellent performance at every stage with F1-scores of 0.97 for
    chart-type classification, 0.91 for text-role classification, and
    a mean Average Precision of 0.95 for text detection.

Comments: 8 Pages, and 11 Figures
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial
          Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2408.16123 [cs.CV]
          (or arXiv:2408.16123v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2408.16123
          Focus to learn more
          arXiv-issued DOI via DataCite
Related   https://doi.org/10.1109/DICTA60407.2023.00082
DOI:      Focus to learn more
          DOI(s) linking to related resources

Submission history

From: Osama Mustafa [view email]
[v1] Wed, 28 Aug 2024 20:22:39 UTC (8,673 KB)
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