Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Irregular scene text detection via attention guided border labeling

Irregular scene text detection via attention guided border labeling Scene text detection plays an important role in many computer vision applications. With the help of recent deep learning techniques, multi-oriented text detection that was considered to be quite challenging has been solved to some extent. However, most existing methods still perform poorly for curved text detection, mainly due to the limitation of their text representations (e.g., horizontal boxes, rotated rectangles or quadrangles). To solve this problem, we propose a novel method to detect irregular scene texts based on instance-aware segmentation. The key idea is to design an attention guided semantic segmentation model to precisely label the weighted borders of text regions. Experiments conducted on several widely-used benchmarks demonstrate that our method achieves superior results on curved text datasets (i.e., with F-score 80.1% and 78.8% for the CTW1500 and Total-Text, respectively) and obtains comparable performance on multi-oriented text datasets compared to the state-of-the-art approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Science China Information Sciences Springer Journals

Irregular scene text detection via attention guided border labeling

Download PDF
11 pages

Loading next page...
 
/lp/springer-journals/irregular-scene-text-detection-via-attention-guided-border-labeling-60wMd1jry8

References (39)

Publisher
Springer Journals
Copyright
Copyright © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Subject
Computer Science; Information Systems and Communication Service
ISSN
1674-733X
eISSN
1869-1919
DOI
10.1007/s11432-019-2673-8
Publisher site
See Article on Publisher Site

Abstract

Scene text detection plays an important role in many computer vision applications. With the help of recent deep learning techniques, multi-oriented text detection that was considered to be quite challenging has been solved to some extent. However, most existing methods still perform poorly for curved text detection, mainly due to the limitation of their text representations (e.g., horizontal boxes, rotated rectangles or quadrangles). To solve this problem, we propose a novel method to detect irregular scene texts based on instance-aware segmentation. The key idea is to design an attention guided semantic segmentation model to precisely label the weighted borders of text regions. Experiments conducted on several widely-used benchmarks demonstrate that our method achieves superior results on curved text datasets (i.e., with F-score 80.1% and 78.8% for the CTW1500 and Total-Text, respectively) and obtains comparable performance on multi-oriented text datasets compared to the state-of-the-art approaches.

Journal

Science China Information Sciences – Springer Journals

Published: Dec 1, 2019

Keywords: scene text detection; weighted border; attention mechanisms; curved text; semantic segmentation

There are no references for this article.