Spatial pyramid pooling enhanced multi-modal linear transformer for table recognition

With the rapid advancement of informatization, tables have become a fundamental format for representing structured data in documents and are widely utilized across various domains, including healthcare, education, and finance. Table recognition aims to automatically convert table content from images into editable and machine-readable formats, significantly enhancing processing efficiency and accuracy. Despite significant advancements made by convolutional neural network (CNN)-based and Transformer-based approaches, existing methods still face challenges in handling complex table layouts, diverse formats, and high computational costs. To address these challenges, we propose the Spatial Pyramid Pooling?enhanced Multi-modal Linear Transformer (SPPLTR), a novel table-recognition model that integrates spatial pyramid pooling with a causal linear-Transformer framework. SPPLTR effectively captures both structural and textual information in table images through a spatial pyramid context fusion module (SPCF) and a causal linear Transformer, thereby improving the understanding of table structures. Additionally, the model incorporates a multi-head causal linear attention (MHCLA) mechanism with automatic mixed precision (AMP) support, enabling more efficient and stable inference. Extensive experiments conducted on the PubTabNet dataset demonstrate that SPPLTR achieves state-of-the-art (SOTA) performance, surpassing most existing models across various evaluation metrics. The proposed model offers a promising solution for table recognition tasks and lays the groundwork for further advancements in structured document understanding. The code for SPPLTR will be made publicly available at https://github.com/Zhanxin-Xin/SPPLTR.