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.
@article{arxiv.2310.17674,
title = {Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis},
author = {Shangbang Long and Siyang Qin and Yasuhisa Fujii and Alessandro Bissacco and Michalis Raptis},
journal= {arXiv preprint arXiv:2310.17674},
year = {2023}
}