Robust Text Detection in Natural Scene Images
Robust Text Detection in Natural Scene Images
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DOI：
10.1109/tpami.2013.182
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发表时间：
2013-01
影响因子：
23.6
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中科院分区：
文献类型：
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自然场景图像中的文本检测是许多基于内容的图像分析任务的重要前提。在本文中，我们提出了一种准确和鲁棒的方法来检测自然场景图像中的文本。设计了一种快速有效的剪枝算法，利用最小化正则化变化的策略提取最大稳定极值区域作为候选字符。通过单链接聚类算法将候选字符分组为候选文本，其中距离权重和聚类阈值由一种新颖的自训练距离度量学习算法自动学习。用字符分类器估计非文本对应的文本候选的后验概率；排除具有高非文本概率的文本候选文本，并用文本分类器识别文本。在ICDAR 2011稳健阅读竞赛数据库上对该系统进行了评估；f值超过76%，比最先进的71%要好得多。在多语言、街景、多方向甚至原生数字数据库上的实验也证明了该方法的有效性。
Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.