Detecting oriented text in natural images by linking segments
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Detecting oriented text in natural images by linking segments. / Shi, Baoguang; Bai, Xiang; Belongie, Serge.
In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 06.11.2017, p. 3482-3490.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Detecting oriented text in natural images by linking segments
AU - Shi, Baoguang
AU - Bai, Xiang
AU - Belongie, Serge
N1 - Funding Information: This work was supported in part by National Natural Science Foundation of China (61222308 and 61573160), a Google Focused Research Award, AWS Cloud Credits for Research, a Microsoft Research Award and a Facebook equipment donation. The authors also thank China Scholarship Council (CSC) for supporting this work Publisher Copyright: © 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line; A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512×512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.
AB - Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line; A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512×512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.
UR - http://www.scopus.com/inward/record.url?scp=85040234078&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.371
DO - 10.1109/CVPR.2017.371
M3 - Conference article
AN - SCOPUS:85040234078
SP - 3482
EP - 3490
JO - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
JF - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -
ID: 301827309