SegHist: A General Segmentation-Based Framework for Chinese Historical Document Text Line Detection

Xingjian Hu, Baole Wei, Liangcai Gao, Jun Wang

Abstract: Text line detection is a key task in historical document analysis facing many challenges of arbitrary-shaped text lines, dense texts, and text lines with high aspect ratios, etc. In this paper, we propose a general framework for historical document text detection (SegHist), enabling existing segmentation-based text detection methods to effectively address the challenges, especially text lines with high aspect ratios. Integrating the SegHist framework with the commonly used method DB++, we develop DB-SegHist. This approach achieves state-of-the-art (SOTA) on the IACC2022CHDAC (CHDAC), MTHv2, and competitive results on ICDAR2019HDRC Chinese (HDRC) datasets, with a significant improvement of 1.19% on the most challenging CHDAC dataset which features more text lines with high aspect ratios. Moreover, our method attains SOTA on rotated MTHv2 and rotated HDRC, demonstrating its rotational robustness.