Enterprise chart question and answer method based on multi modal cross fusion - Xinxin Wang; Liang Chen; Changhong Liu; Jinyu Liu (Scientific Reports)
Abstract To enhance enterprises’ interactive exploration capabilities for unstructured chart data, this paper proposes a multimodal chart question-answering method. Facing the challenge of recognizing curved and irregular text in charts, we introduce Gaussian heatmap encoding technology to achieve character-level precise text annotation. Additionally, we combine a key point detection algorithm to extract numerical information from the charts and convert it into structured table data. Finally, by employing a multimodal cross-fusion model, we deeply integrate the queried charts, user questions, and generated table data to ensure that the model can comprehensively capture chart information and accurately answer user questions. Experimental validation has demonstrated that our method achieves a precision of 91.58% in chart information extraction and a chart question-answering accuracy of 82.24%, fully proving the significant advantages of our proposed method in enhancing chart text recognition and question-answering capabilities. Through practical enterprise application cases, our method has shown its ability to answer four types of chart questions, exhibiting mathematical reasoning capabilities and providing robust support for enterprise data analysis and decision-making.