DTSM: Toward Dense Table Structure Recognition with Text Query Encoder and Adjacent Feature Aggregator

Xinhong Chen, Bangdong Chen, Chenfan Qu, Dezhi Peng, Chongyu Liu, Lianwen Jin

Abstract: In recent years, significant progress has been made in table structure recognition, yet the recognition of structures within dense tables remains a challenge that has been largely overlooked. To address this gap, we introduce DenseTab, a new dataset consisting of 16,575 dense tables with comprehensive annotation information that includes physical position, logical position, and text content of each cell, along with the HTML sequence. To tackle the challenge of dense table structure recognition, we propose a new method called Dense Table Splitting and Merging Model (DTSM). DTSM includes a novel text query encoder to leverage layout information associated with the text’s location, and an adjacent feature aggregator to enhance the prediction of cell merging information. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in recognizing dense table structures. The dataset and code is available at  https://github.com/TenMilesLotus/DTSM.