ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules

• URL: http://arxiv.org/abs/2304.02173v1
• Date: Wed, 5 Apr 2023 00:25:27 GMT
• Title: ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules
• Authors: Zhi-Qi Cheng, Qi Dai, Siyao Li, Jingdong Sun, Teruko Mitamura, Alexander G. Hauptmann
• Abstract summary: We present ChartReader, a unified framework that seamlessly integrates chart derendering and comprehension tasks. Our approach includes a transformer-based chart component detection module and an extended pre-trained vision-language model for chart-to-X tasks. Our proposed framework can significantly reduce the manual effort involved in chart analysis, providing a step towards a universal chart understanding model.
• Score: 89.75395046894809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Charts are a powerful tool for visually conveying complex data, but their comprehension poses a challenge due to the diverse chart types and intricate components. Existing chart comprehension methods suffer from either heuristic rules or an over-reliance on OCR systems, resulting in suboptimal performance. To address these issues, we present ChartReader, a unified framework that seamlessly integrates chart derendering and comprehension tasks. Our approach includes a transformer-based chart component detection module and an extended pre-trained vision-language model for chart-to-X tasks. By learning the rules of charts automatically from annotated datasets, our approach eliminates the need for manual rule-making, reducing effort and enhancing accuracy.~We also introduce a data variable replacement technique and extend the input and position embeddings of the pre-trained model for cross-task training. We evaluate ChartReader on Chart-to-Table, ChartQA, and Chart-to-Text tasks, demonstrating its superiority over existing methods. Our proposed framework can significantly reduce the manual effort involved in chart analysis, providing a step towards a universal chart understanding model. Moreover, our approach offers opportunities for plug-and-play integration with mainstream LLMs such as T5 and TaPas, extending their capability to chart comprehension tasks. The code is available at https://github.com/zhiqic/ChartReader.

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