Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
This addresses the problem of comprehensive text understanding in images for applications like document analysis, though it appears incremental as it builds on existing text spotting and layout analysis methods.
The paper tackles the joint task of word-level text spotting and geometric layout analysis by proposing Hierarchical Text Spotter (HTS), which recognizes text and identifies a 4-level hierarchical structure (characters, words, lines, paragraphs), achieving state-of-the-art results on multiple benchmark datasets.
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.