LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding

Min Liang, Jia-Wei Ma, Xiaobin Zhu, Jingyan Qin, Xu-Cheng Yin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15665-15674

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.

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[bibtex]
@InProceedings{Liang_2024_CVPR, author = {Liang, Min and Ma, Jia-Wei and Zhu, Xiaobin and Qin, Jingyan and Yin, Xu-Cheng}, title = {LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {15665-15674} }