Effective Candidate Component Extraction for Text Localization in Born-Digital Images by Combining Text Contours and Stroke Interior Regions
Chen, Kai; Yin, Fei; Liu, Chenglin
2016-04
会议名称Proceedings of 12th IAPR International Workshop on Document Analysis Systems
会议录名称Proceedings of IAPR International Workshop on Document Analysis Systems
会议日期2016-4
会议地点希腊
摘要Extracting candidate text connected components
(CCs) is critical for CC-based text localization. Based on the
observation that text strokes in born-digital images mostly have
complete contours and the text pixels have high contrast with
the adjacent non-text pixels, we propose a method to extract
candidate text CCs by combining text contours and stroke
interior regions. After segmenting the image into non-smooth
and smooth regions based on local contrast, text contour pixels in
non-smooth regions are detached from adjacent non-text pixels
by local binarization. Then, obvious non-text contours can be
removed according to the spatial relationship of text and non-text
contours. While smooth regions include stroke interior regions
and non-text smooth regions, some non-text smooth regions
can be easily removed because they are not surrounded by
candidate text contours. At last, candidate text contours and
stroke interior regions are combined to generate candidate text
CCs. The CCs undergo CC filtering, text 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.
关键词Candidate Component Extraction Text Contours Stroke Interior Regions Text Localization.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11949
专题模式识别国家重点实验室_模式分析与学习
通讯作者Chen, Kai
作者单位中科院自动化所
推荐引用方式
GB/T 7714
Chen, Kai,Yin, Fei,Liu, Chenglin. Effective Candidate Component Extraction for Text Localization in Born-Digital Images by Combining Text Contours and Stroke Interior Regions[C],2016.
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