START: Spatial and Textual Learning for Chart Understanding

Zhuoming Liu; Xiaofeng Gao; Feiyang Niu; Qiaozi Gao; Liu Liu; Robinson Piramuthu

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arXiv · 2512.07186

START: Spatial and Textual Learning for Chart Understanding

Zhuoming Liu·Xiaofeng Gao·Feiyang Niu·Qiaozi Gao·Liu Liu·Robinson Piramuthu

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Abstract

Chart understanding is crucial for deploying multimodal large language models (MLLMs) in real-world scenarios such as analyzing scientific papers and technical reports. Unlike natural images, charts pair a structured visual layout (spatial property) with an underlying data representation (textual property) -- grasping both is essential for precise, fine-grained chart reasoning. Motivated by this observation, we propose START, the Spatial and Textual learning for chART understanding. Specifically, we introduce (i) chart-element grounding and (ii) chart-to-code generation to strengthen an MLLM's understanding of both chart visual layout and data details. To facilitate spatial and textual learning, we propose the START-Dataset generated with a novel data-generation pipeline that first leverages an MLLM to translate real chart images into executable chart code, recovering the underlying data representation while preserving the visual distribution of real-world charts. We then evolve the code with a Large Language Model (LLM) to ascertain the positions of chart elements that capture the chart's visual structure, addressing challenges that existing methods cannot handle. To evaluate a model's ability to understand chart spatial structures, we propose the Chart Spatial understanding Benchmark (CS-Bench), filling a critical gap in comprehensive chart understanding evaluation. Leveraging spatial and textual learning, START delivers consistent gains across model sizes and benchmarks over the base models and surpasses prior state-of-the-art by a clear margin. Code, data and models will be publicly available.

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BibTeX RIS

Zhuoming Liu, Xiaofeng Gao, Feiyang Niu, Qiaozi Gao, Liu Liu, Robinson Piramuthu. 2025-12-08. START: Spatial and Textual Learning for Chart Understanding. https://arxiv.org/abs/2512.07186

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