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
ingle-link clustering algorithm, where distance weights and threshold of the
clustering algorithm 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 an character
classifier; text candidates with high probabilities are then eliminated and
finally texts are identified with a text classifier. The proposed system is
evaluated on the ICDAR 2011 Robust Reading Competition dataset; the f measure
is over 76% and is significantly better than the state-of-the-art performance
of 71%. Experimental results on a publicly available multilingual dataset also
show that our proposed method can outperform the other competitive method with
the f measure increase of over 9 percent. Finally, we have setup an online demo
of our proposed scene text detection system at
this http URL

本文提出了一种准确和鲁棒性较高的文本检测方法，通过设计快速有效的剪枝算法，使用最小化正则化变化策略来提取极值区域，然后通过单链接聚类算法将字符候选分组为文本候选，在使用一种自学习距离度量算法自动学习距离权重和聚类算法的阈值来识别文本候选，并使用字符分类器估计相应的文本候选的后验概率来消除具有高概率的非文本文本候选，并最终识别出文本。通过实验结果证明，该方法的检测效果优于其他竞争方法。