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.

我们提出了一种名为层级文本识别器（HTS）的新方法，用于识别图像中的文本并确定其字符、单词、行和段落的四级层级结构。HTS 通过两个新颖组件实现：(1) 统一检测器多边形（UDP），可以生成文本行的贝塞尔曲线多边形以及用于检测线之间自然分组的亲和矩阵；(2) 行 - 字符 - 单词（L2C2W）识别器，可以将行分割为字符，再将它们组合成单词。HTS 在多个单词级文本识别基准数据集以及几何布局分析任务中取得了最先进的结果。