Towards End-to-End Unified Scene Text Detection and Layout Analysis
Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis
Abstract
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.
BibTeX
@inproceedings{cvpr2022_towardsendtoendu,
title = {Towards End-to-End Unified Scene Text Detection and Layout Analysis},
author = {Shangbang Long and Siyang Qin and Dmitry Panteleev and Alessandro Bissacco and Yasuhisa Fujii and Michalis Raptis},
booktitle = {CVPR 2022},
year = {2022}
}