LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding

Min Liang, Jia-Wei Ma, Xiaobin Zhu, Jingyan Qin, Xu-Cheng Yin

Abstract: Existing scene text detectors generally focus on accu-rately detecting single-level (i.e., word-level, line-level, or paragraph-level) text entities without exploring the relationships among different levels of text entities. To comprehensively understand scene texts, detecting multi-level texts while exploring their contextual information is criti-cal. To this end, we propose a unified framework (dubbed LayoutFormer) for hierarchical text detection, which simultaneously conducts multi-level text detection and predicts the geometric layouts for promoting scene text understanding. In LayoutFormer, WordDecoder, LineDecoder, and Pa- raDecoder are proposed to be responsible for word-level text prediction, line-level text prediction, and paragraph- level text prediction, respectively. Meanwhile, WordDe-coder and ParaDecoder adaptively learn word-line and line-paragraph relationships, respectively. In addition, we propose a Prior Location Sampler to be used on multi-scale features to adaptively select a few representative foreground features for updating text queries. It can improve hierar- chical detection performance while significantly reducing the computational cost. Comprehensive experiments verify that our method achieves state-of-the-art performance on single-level and hierarchical text detection.

External IDs:dblp:conf/cvpr/LiangM0QY24