Paper Info
Title
Scene Text Detection Based on Expanding the Text Center Region for Bilingual Tibetan-Chinese
Abstract
Scene text detection is an important research branch of artificial intelligence technology whose goal is to locate text in scene images. In the Tibetan areas of China, scene images containing both Tibetan and Chinese texts are ubiquitous. Thus, detecting bilingual Tibetan-Chinese scene texts is important in promoting intelligent applications for minority languages. In this study, a scene text detection database for bilingual Tibetan-Chinese is constructed using a manually labeled method and an automatic synthesis method, and a text detection method is proposed. First, we predict a text rectangular region and the text center region for each text instance and simultaneously learned the expansion distance of the text center region. Second, based on the classification score of the text center region and the text rectangular region, we obtain the final confidence of each text instance and then filter out the text center region with lower confidence. Third, through the learned expansion distance, the full-text instance from the remaining text center region is recovered. The results show that our method obtains good detection performance; it achieves an accuracy of up to 75.47% during the text detection phase, laying the foundation for scene text recognition in the subsequent step.
Year
DOI
Venue
2021
10.1142/S0218001421530074
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Tibetan-Chinese bilingual, scene text detection, region expansion, scenes text detection database
Journal
35
Issue
ISSN
Citations 
13
0218-0014
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jincheng Li100.34
Yusheng Hao200.34
Weilan Wang3911.75
Tiejun Wang400.34
Qiaoqiao Li500.34