Table-MCR2TR: Merged-Cell-Aware Table Recognition via Reinforced Multimodal Language Models

Table recognition (TR) aims to transcribe table images into semi-structured representations (e.g., HTML or Markdown). However, current methods still struggle with complex structures, particularly involving merged cells, which are essential for accurate table parsing and downstream understanding tasks. While some works improve TR through large-scale training and global supervision, these local fine-grained yet crucial merged-cell attributes are often overlooked, becoming a key bottleneck for further progress. To tackle this, we introduce Table-MCR2TR, a reinforced multimodal large language model framework that leverages the enhanced merged-cell recognition (MCR) ability as contextual guidance to improve table recognition quality. To enable this capability, we first develop a high-fidelity data pipeline to construct a large-scale table dataset covering many complex merged cells, alleviating their scarcity in existing datasets. Additionally, the merged-cell-aware table recognition (MCATR) optimization strategy is proposed to improve table parsing via collaborative reinforcement learning. The core of MCATR lies in integrating three complementary objectives: TR and MCR respectively learn the global table structure and fine-grained merged-cell information; based on these learned signals, the MCR2TR alignment task transfers merged-cell knowledge to guide TR generation. Experimental results show that Table-MCR2TR achieves SOTA across multiple benchmarks, with average TR/MCR accuracy of 92.3%/83.9%, outperforming top-tier models and further boosting downstream table QA performance.