Table structure recognition (TSR), the task of extracting logical and physical structures from table images, is critical for document understanding. Current end-to-end image-to-text methods typically employ a top-down strategy where physical structure prediction depends on the logical decoder's output sequence. However, this process often suffers from training instability and misalignment between predicted bounding boxes and ground-truth cell positions. To address this issue, we propose G2LFormer, a novel transformer-based framework that employs a ''Global-to-Local'' query enhancement strategy. Specifically, G2LFormer introduces a Vision-guided Query Enhancer to integrate both textual and visual modalities, significantly improving the overall query representation capability and boosting prediction accuracy. Additionally, we design a Multi-scale Manhattan Vision-guider that leverages a spatial attenuation matrix to guide each query towards its corresponding cell location, effectively balancing local and global information for more precise bounding box generation. Extensive experiments on benchmark datasets demonstrate G2LFormer's superior performance, while ablation studies confirming the significant contribution of each proposed module in achieving state-of-the-art results. The source code and model have been released at: https://github.com/Hzbupahaozi/G2LFormer.