In this paper, we propose a novel algorithm to detect text information from natural scene images. Scene text classification and detection are still open research topics. Our proposed algorithm is able to model both character appearance and structure to generate representative and discriminative text descriptors. The contributions of this paper include three aspects: 1) a new character appearance model by a structure correlation algorithm which extracts discriminative appearance features from detected interest points of character samples; 2) a new text descriptor based on structons and correlatons, which model character structure by structure differences among character samples and structure component co-occurrence; and 3) a new text region localization method by combining color decomposition, character contour refinement, and string line alignment to localize character candidates and refine detected text regions. We perform three groups of experiments to evaluate the effectiveness of our proposed algorithm, including text classification, text detection, and character identification. The evaluation results on benchmark datasets demonstrate that our algorithm achieves the state-of-the-art performance on scene text classification and detection, and significantly outperforms the existing algorithms for character identification.

中文翻译：

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通过字符外观和结构建模从场景图像中提取文本。

在本文中，我们提出了一种从自然场景图像中检测文本信息的新算法。场景文本分类和检测仍然是开放的研究课题。我们提出的算法能够对字符外观和结构进行建模，以生成具有代表性和区分性的文本描述符。本文的贡献包括三个方面：1）通过结构相关算法从检测到的字符样本的兴趣点中提取有区别的外观特征的一种新的字符外观模型；2）一种基于结构和相关的新文本描述符，它通过字符样本之间的结构差异和结构组件共现对字符结构进行建模；3）一种新的文本区域定位方法，结合颜色分解、字符轮廓细化，和字符串行对齐以定位候选字符并细化检测到的文本区域。我们进行了三组实验来评估我们提出的算法的有效性，包括文本分类、文本检测和字符识别。在基准数据集上的评估结果表明，我们的算法在场景文本分类和检测方面达到了最先进的性能，并且明显优于现有的字符识别算法。