ChartEye: A Deep Learning Framework for Chart Information Extraction
<|reference_start|>ChartEye: A Deep Learning Framework for Chart Information Extraction: 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.<|reference_end|>
@article{mustafa2024charteye:,
    title={ChartEye: A Deep Learning Framework for Chart Information Extraction},
    author={Osama Mustafa, Muhammad Khizer Ali, Momina Moetesum, Imran Siddiqi},
    journal={arXiv preprint arXiv:2408.16123},
    year={2024},
    doi={10.1109/DICTA60407.2023.00082},
    archivePrefix={arXiv},
    eprint={2408.16123},
    primaryClass={cs.CV cs.AI cs.LG}
}