Papers › Towards End-to-End Unified Scene Text Detection and Layout Analysis

Towards End-to-End Unified Scene Text Detection and Layout Analysis

28 Mar 2022CVPR 2022 1arXiv:2203.15143archive 2025-07-28

Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis

Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. Comprehensive experiments show that our unified model achieves better performance than multiple well-designed baseline methods. Additionally, this model achieves state-of-the-art results on multiple scene text detection datasets without the need of complex post-processing. Dataset and code: https://github.com/google-research-datasets/hiertext and https://github.com/tensorflow/models/tree/master/official/projects/unified_detector.

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