PyramidTabNet++: Table Detection with Pyramid Vision Transformer and Boundary Refinement

作者：Ha Thi Hien, Thanh Trung Cao, Hai‐Hong Phan · 年份：2025 · DOI：10.1109/rivf68649.2025.11365062 · 研究领域：Handwritten Text Recognition Techniques、Advanced Neural Network Applications、Currency Recognition and Detection

Table detection and structure recognition are fundamental tasks in document image analysis, as they play a critical role in information extraction from digital and scanned documents. Yet, existing methods often suffer from imprecise localization and noisy boundary predictions. Building on the success of PyramidTabNet (PTN), we propose PyramidTabNet++ (PTN++), which integrates a Pyramid Vision Transformer backbone, a Cascade R-CNN detector, and an enhanced boundary line correction module for table detection. This combination leverages the strengths of transformer-based feature extraction, multi-stage object detection, and geometry-aware refinement to achieve more precise and reliable table localization. Extensive experiments on public benchmarks, including ICDAR-2017 POD, ICDAR-2019, and TableBank, demonstrate that PTN++ achieves accurate and robust performance across diverse datasets.