mlsanityData to 7 Oct 2026

SegHist: A General Segmentation-based Framework for Chinese Historical Document Text Line Detection

arXiv 2406.15485 ↗First posted 17 Jun 2024cs.CL cs.CVcited by 2

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Abstract (arXiv)

Text line detection is a key task in historical document analysis facing many challenges of arbitrary-shaped text lines, dense texts, and text lines with high aspect ratios, etc. In this paper, we propose a general framework for historical document text detection (SegHist), enabling existing segmentation-based text detection methods to effectively address the challenges, especially text lines with high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 SOTA on the CHDAC, MTHv2, and competitive results on 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. The code is available at https://github.com/LumionHXJ/SegHist.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 51 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

35 of 51 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026EpiSAM: Character Segmentation in Challenging Stone Inscriptionscited by 1
  2. 2026Text region detection in historical astronomical diagramscited by 0