标题:Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification
作者:Yan, Chenggang; Xie, Hongtao; Liu, Shun; Yin, Jian; Zhang, Yongdong; Dai, Qionghai
作者机构:[Yan, Chenggang] Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310018, Zhejiang, Peoples R China.; [Xie, Hongtao; Zhang, Yongdong] Univ Sc 更多
通讯作者:Xie, Hongtao
通讯作者地址:[Xie, HT]Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Anhui, Peoples R China.
来源:IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版年:2018
卷:19
期:1
页码:220-229
DOI:10.1109/TITS.2017.2749977
关键词:Smart transportation; intelligent vehicles; Uyghur text detection; the; channel-enhanced MSER
摘要:Text detection in complex background images is a challenging task for intelligent vehicles. Actually, almost all the widely-used systems focus on commonly used languages while for some minority languages, such as the Uyghur language, text detection is paid less attention. In this paper, we propose an effective Uyghur language text detection system in complex background images. First, a new channel-enhanced maximally stable extremal regions (MSERs) algorithm is put forward to detect component candidates. Second, a two-layer filtering mechanism is designed to remove most non-character regions. Third, the remaining component regions are connected into short chains, and the short chains are extended by a novel extension algorithm to connect the missed MSERs. Finally, a two-layer chain elimination filter is proposed to prune the non-text chains. To evaluate the system, we build a new data set by various Uyghur texts with complex backgrounds. Extensive experimental comparisons show that our system is obviously effective for Uyghur language text detection in complex background images. The F-measure is 85%, which is much better than the state-of-the-art performance of 75.5%.
收录类别:EI;SCIE
WOS核心被引频次:29
资源类型:期刊论文
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