@inproceedings{dcde3d7e40ea482c98f5ac6763e05698,
title = "Multi-scale FCN with cascaded instance aware segmentation for arbitrary oriented word spotting in the wild",
abstract = "Scene text detection has attracted great attention these years. Text potentially exist in a wide variety of images or videos and play an important role in understanding the scene. In this paper, we present a novel text detection algorithm which is composed of two cascaded steps: (1) a multiscale fully convolutional neural network (FCN) is proposed to extract text block regions; (2) a novel instance (word or line) aware segmentation is designed to further remove false positives and obtain word instances. The proposed algorithm can accurately localize word or text line in arbitrary orientations, including curved text lines which cannot be handled in a lot of other frameworks. Our algorithm achieved state-of-the-art performance in ICDAR 2013 (IC13), ICDAR 2015 (IC15) and CUTE80 and Street View Text (SVT) benchmark datasets.",
author = "Dafang He and Xiao Yang and Chen Liang and Zihan Zhou and Ororbia, {Alex G.} and Daniel Kifer and Giles, {C. Lee}",
note = "Funding Information: This work was supported by NSF grant CCF 1317560 and a hardware grant from NVIDIA. Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",
year = "2017",
month = nov,
day = "6",
doi = "10.1109/CVPR.2017.58",
language = "English (US)",
series = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "474--483",
booktitle = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",
address = "United States",
}