基于分层多层感知器的场景文本检测

Gang Zhou, Yuehu Liu, Jianji Wang
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引用次数: 2

摘要

本文提出了一种基于分层多层感知器(MLP)的场景文本检测方法。首先,利用文本概率图对连通分量进行局部分割;然后,利用由两个MLP分类器串联组成的新颖层次结构对cc进行分析。在这种分层设置中,第一阶段MLP分类器使用一元属性特征进行训练。第二阶段MLP分类器训练CCs对,包括第一阶段估计的后验概率和关系特征。最后,候选文本cc被分组成单词。在公共数据集上评估的实验结果表明,与最先进的方法相比,我们的方法产生了更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scene text detection based on hierarchical multilayer perceptron
In this paper, a new scene text detection method based on hierarchical multilayer perceptron (MLP) is proposed. First, connected components (CCs) are segmented locally by text probability map. Then, a novelty hierarchical architecture consisting of two MLP classifiers in tandem is utilized to analysis the CCs. In this hierarchical setup, the first stage MLP classifier is trained using unary property features. The second stage MLP classifier is trained for CCs pairs including both posterior probabilities estimated by first stage and relationship features. Finally, candidate text CCs are grouping into words. Experimental results evaluated on the public dataset show that our approach yields better performance compared with state-of-the-art methods.
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