Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification
Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification
复制标题
复杂背景图像中有效维吾尔语文本检测，实现交通提示识别
DOI：
10.1109/tits.2017.2749977
复制
发表时间：
2018-01-01
影响因子：
8.5
通讯作者：
Dai, Qionghai
中科院分区：
文献类型：
--
作者：
Yan, Chenggang;Xie, Hongtao;Dai, Qionghai
复杂背景图像中的文字检测是智能车辆面临的一项挑战性任务。实际上，几乎所有广泛使用的系统都集中在常用语言上，而对于一些少数民族语言，如维吾尔语，文本检测关注较少。本文提出了一种有效的复杂背景图像中维吾尔语文本检测系统。首先，提出了一种新的通道增强的最大稳定极值区域（MSERs）算法来检测候选成分。第二，设计了两层过滤机制，以去除大部分非字符区域。第三，剩余的组件区域连接成短链，并通过一种新的扩展算法来扩展短链，以连接丢失的MSER。最后，提出了一种两层链消除过滤器来修剪非文本链。为了评估该系统，我们建立了一个新的数据集的各种维吾尔语文本与复杂的背景。大量的实验比较表明，我们的系统是明显有效的复杂背景图像中的维吾尔语文本检测。F-测量值为85%，这比最先进的75.5%的性能要好得多。
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%.