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
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
Corresponding AuthorKai Chen
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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