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Computer Science > Computer Vision and Pattern Recognition

arXiv:2310.17674 (cs)
[Submitted on 25 Oct 2023]

Title:Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis

Authors:Shangbang Long, Siyang Qin, Yasuhisa Fujii, Alessandro Bissacco, Michalis Raptis
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Abstract:We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis. HTS can recognize text in an image and identify its 4-level hierarchical structure: characters, words, lines, and paragraphs. The proposed HTS is characterized by two novel components: (1) a Unified-Detector-Polygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines; (2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words. HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks.
Comments: Accepted to WACV 2024
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2310.17674 [cs.CV]
  (or arXiv:2310.17674v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2310.17674

Submission history

From: Shangbang Long [view email]
[v1] Wed, 25 Oct 2023 22:23:54 UTC (8,494 KB)
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