  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.
