OneChart: Purify the Chart Structural Extraction via One Auxiliary Token
Jinyue Chen Lingyu Kong Haoran Wei Chenglong Liu Zheng Ge Liang Zhao Jianjian Sun Chunrui Han Xiangyu Zhang
Chart parsing poses a significant challenge due to the diversity of styles,
values, texts, and so forth. Even advanced large vision-language models (LVLMs)
with billions of parameters struggle to handle such tasks satisfactorily. To
address this, we propose OneChart: a reliable agent specifically devised for
the structural extraction of chart information. Similar to popular LVLMs,
OneChart incorporates an autoregressive main body. Uniquely, to enhance the
reliability of the numerical parts of the output, we introduce an auxiliary
token placed at the beginning of the total tokens along with an additional
decoder. The numerically optimized (auxiliary) token allows subsequent tokens
for chart parsing to capture enhanced numerical features through causal
attention. Furthermore, with the aid of the auxiliary token, we have devised a
self-evaluation mechanism that enables the model to gauge the reliability of
its chart parsing results by providing confidence scores for the generated
content. Compared to current state-of-the-art (SOTA) chart parsing models,
e.g., DePlot, ChartVLM, ChartAst, OneChart significantly outperforms in Average
Precision (AP) for chart structural extraction across multiple public
benchmarks, despite enjoying only 0.2 billion parameters. Moreover, as a chart
parsing agent, it also brings 10%+ accuracy gains for the popular LVLM
(LLaVA-1.6) in the downstream ChartQA benchmark.