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Charts are a powerful tool for visually conveying complex data, but theircomprehension poses a challenge due to the diverse chart types and intricatecomponents. Existing chart comprehension methods suffer from either heuristicrules or an over-reliance on OCR systems, resulting in suboptimal performance.To address these issues, we present ChartReader, a unified framework thatseamlessly integrates chart derendering and comprehension tasks. Our approachincludes a transformer-based chart component detection module and an extendedpre-trained vision-language model for chart-to-X tasks. By learning the rulesof charts automatically from annotated datasets, our approach eliminates theneed for manual rule-making, reducing effort and enhancing accuracy.~We alsointroduce a data variable replacement technique and extend the input andposition embeddings of the pre-trained model for cross-task training. Weevaluate ChartReader on Chart-to-Table, ChartQA, and Chart-to-Text tasks,demonstrating its superiority over existing methods. Our proposed framework cansignificantly reduce the manual effort involved in chart analysis, providing astep towards a universal chart understanding model. Moreover, our approachoffers opportunities for plug-and-play integration with mainstream LLMs such asT5 and TaPas, extending their capability to chart comprehension tasks. The codeis available at https://github.com/zhiqic/ChartReader.

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