Recently, interpreting complex charts with logical reasoning have emerged as challenges due to the development of vision-language models. A prior state-of-the-art (SOTA) model, Deplot, has presented an end-to-end method that leverages the vision-language model to convert charts into table format utilizing Large Language Models (LLMs) for reasoning. However, unlike natural images, charts contain a mix of essential and irrelevant information required for chart reasoning, and we discover that this characteristic can lower the performance of chart-to-table extraction. In this paper, we introduce SIMPLOT, a method designed to extract only the elements necessary for chart reasoning. The proposed method involves two steps: 1) training to mimic a simple plot that contains only the essential information from a complex chart for table extraction, followed by 2) performing reasoning based on the table. Our model enables accurate chart reasoning without the need for additional annotations or datasets, and its effectiveness is demonstrated through various experiments. Furthermore, we propose a novel prompt addressing the shortcoming of recent SOTA model, ignoring visual attributes such as color. Our source code is available at https://github.com/sangwu99/Simplot.

我们提出了一种名为SIMPLOT的方法，该方法能够仅提取图表推理所需的要素，通过对表格进行推理实现准确的图表推理，并解决了最近一种最先进的模型忽略颜色等视觉属性的缺点。

SIMPLOT：通过提炼要点增强图表问答