LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding

Abstract

Existing scene text detectors generally focus on accurately 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 critical. 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 ParaDecoder are proposed to be responsible for word-level text prediction line-level text prediction and paragraph-level text prediction respectively. Meanwhile WordDecoder 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 hierarchical 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.