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ChartEye: A Deep Learning Framework for Chart Information Extraction (2408.16123v1)

Published 28 Aug 2024 in cs.CV, cs.AI, and cs.LG

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.

Citations (1)

Summary

  • The paper introduces a deep learning framework that achieves a 0.97 F1-score in chart-type classification while accurately detecting and classifying text roles.
  • It employs a multi-step methodology combining Swin Transformer, YOLOv7, ESRGAN, and TPS-ResNet to enhance chart image interpretation and resolution.
  • The strong experimental results on the ICPR2022 dataset underscore its potential for applications in automated report generation and intelligent data dashboards.

ChartEye: A Deep Learning Framework for Chart Information Extraction

The paper "ChartEye: A Deep Learning Framework for Chart Information Extraction" presents a sophisticated pipeline for automated extraction of information from chart images, focusing on explicit data. The necessity of this research stems from the pervasive use of charts and infographics across various domains to visually convey complex data patterns. While previous efforts have been made to automate the interpretation of these charts, challenges due to style, layout variations, and text recognition persist. The authors propose a comprehensive deep learning framework that addresses key tasks: chart-type classification, text detection, text resolution enhancement, text recognition, and text-role classification.

Methodology

The proposed framework is divided into several well-defined steps to handle the varying complexities of chart interpretation:

  1. Chart-Type Classification:
    • The paper employs a Swin Transformer, known for its hierarchical feature extraction and contextual learning. This architecture, pretrained on ImageNet, was fine-tuned to classify 15 different chart types, achieving an F1-score of 0.97. The self-attention mechanisms and hierarchical learning capabilities of the Swin Transformer yield robust performance even in the face of varied chart structures and semantics.
  2. Text Detection:
    • The paper utilizes the YOLOv7 architecture, a single-stage object detector, for text detection due to its efficiency and high precision. YOLOv7’s design minimizes time complexity while maintaining high detection accuracy, achieving a mean Average Precision (mAP) of 0.95 across different chart types.
  3. Text Resolution Enhancement:
    • To improve the recognition of small and low-resolution text, the paper integrates the ESRGAN for text upscaling. This enhancement is particularly beneficial for optical character recognition (OCR) tasks, significantly improving the legibility of detected text and consequently the performance of subsequent recognition tasks.
  4. Text Recognition:
    • For recognizing the text within the charts, the paper uses the TPS-ResNet-BiLSTM-Attn model, which combines thin-plate spline (TPS) transformation, a ResNet backbone, and a bi-directional LSTM with an attention mechanism. This model effectively handles the diverse geometric distortions of text within charts, improving recognition accuracy post-resolution enhancement.
  5. Text-Role Classification:
    • The role of each detected text segment is classified using a fine-tuned Swin Transformer. This step is crucial for semantic parsing of the chart, identifying roles such as titles, labels, and values. The Swin Transformer here exploits positional information and relational context among different text segments, achieving an F1-score of 0.91 for this task.

Experimental Evaluation

The experimental evaluation employs the ICPR2022 CHARTINFO UB PMC competition dataset, covering 15 different chart types with substantial variations across each type. The results obtained demonstrate the efficacy of the proposed framework. Notably:

  • The chart-type classification model, based on Swin Transformer, outperforms classical architectures like ResNet and recent ensembles, with a notable F1-score of 0.97.
  • YOLOv7 achieves consistent high performance in text detection across multiple chart types, confirming its applicability in scenarios requiring precise text localization.
  • The integration of ESRGANs for text resolution enhancement shows a substantial impact on the quality of text recognition, particularly for OCR tasks where conventional tools like Tesseract struggle with low-resolution inputs.
  • The Swin Transformer for text-role classification manages the complexities arising from the positional and relational variances of chart text, significantly outperforming alternative models like YOLOv7 in this classification task.

Implications and Future Directions

The proposed framework demonstrates its potential in efficiently parsing and understanding chart images, which can have significant implications for the field of Document AI. The strong numerical results indicate that attention-based and transformer architectures can be pivotal in addressing complex visual and textual information extraction tasks.

In practical terms, this framework can enhance the analytic capabilities of numerous applications, ranging from automated report generation to intelligent data dashboards and beyond. The inclusion of ESRGANs for text resolution enhancement suggests that image quality improvement methods can substantially bolster OCR systems' performance, a direction worth exploring further.

Future developments could involve extending this framework to handle more diverse and complicated chart types, enhancing robustness against noisy and occluded data, and integrating more sophisticated relational learning models. Additionally, the exploration of unsupervised and semi-supervised learning approaches could further generalize the model’s applicability across various unlabeled datasets.

In conclusion, the methodology and results presented in this paper underscore the substantial headway being made in automated chart information extraction, leveraging advanced deep learning techniques to tackle longstanding challenges in the domain.

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