CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Efficient Text Localization in Born-Digital Images by Local Contrast-Based Segmentation
Kai Chen; Yin, Fei; Liu, Chenglin
2015-08
Conference Name2015 13TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)
Source PublicationInternational Conference on Document Analysis and Recognition (ICDAR)
Conference Date2015-8
Conference Place法国南锡
AbstractText localization in born-digital images is usually
performed using methods designed for scene text images. Based
on the observation that text strokes in born-digital images mostly
have complete contours and the pixels on the contours have
high contrast compared with the adjacent non-text pixels, we
propose a method to extract candidate text components using
local contrast. First, the image is segmented into smooth and
non-smooth regions. After removing non-text smooth regions, the
remaining smooth regions are merged with non-smooth regions
to form a candidate text image, which is binarized into high-value
and low-value connected components (CCs). The CCs undergo
CC filtering, line grouping and line classification to give the
text localization result. Experimental results on the born-digital
dataset of ICDAR2013 robust reading competition demonstrate
the efficiency and superiority of the proposed method.
KeywordText Localization Image Segmentation Local Contrast Connected Components Grouping
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11948
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorKai Chen
Affiliation中科院自动化研究所
Recommended Citation
GB/T 7714
Kai Chen,Yin, Fei,Liu, Chenglin. Efficient Text Localization in Born-Digital Images by Local Contrast-Based Segmentation[C],2015.
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