Lightweight and Accurate Table Recognition via Improved SLANet with Multi-Phase Training Strategy

Tables, as an efficient form of structured data representation, are widely applied across domains. However, traditional manual processing methods are inadequate in the big data era, and existing table recognition models, such as SLANet, still face performance limitations. To address these issues, this paper proposes an improved SLANet framework. First, the original H-Swish activation is replaced with the Mish function to enhance feature representation. Second, an end-of-sequence (EOS) termination mechanism is introduced to reduce computational redundancy during inference. Third, a three-phase training strategy is designed to achieve progressive performance improvements. Experimental evaluation on the PubTabNet benchmark demonstrates that the improved SLANet achieves 77.25% accuracy with an average inference time of 774 ms, outperforming the baseline and most mainstream algorithms while retaining lightweight efficiency. The proposed algorithm achieves a TEDS score of 96.67%, significantly surpassing SLANet-based and other state-of-the-art methods. The code will be released upon acceptance.