摘要翻译：
场景文本是一个重要的特征提取，特别是在基于视觉的移动机器人导航中，许多潜在的地标，如铭牌、信息标志等都含有文本。本文介绍了一种新颖的用于室内移动机器人导航的两步文本定位方法。该方法基于形态学算子和机器学习技术，可用于实时环境。提出的方法有两个步骤。首先，利用一组新的形态学算子和一个特定的序列来提取高对比度区域，这些区域具有较高的文本存在概率。使用形态学算子具有计算速度快、对平移、旋转、缩放等几何变换不变、能够提取所有包含文本的区域等优点。在提取文本候选区域后，提取一组九个特征，用于准确地检测和删除没有文本的区域。这些特征是纹理属性的描述符，是实时计算的。然后，我们使用一个支持向量机分类器来检测区域中文本的存在。将该算法与几种常用的文本定位算法进行了性能比较，结果表明，该算法能够快速有效地从真实场景中定位和提取文本区域，可用于室内环境下移动机器人导航中基于文本的地标检测。
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英文标题：
《Real time text localization for Indoor Mobile Robot Navigation》
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作者：
Kazem Qazanfari, Saeed Shiri
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. In this paper, a novel two-step text localization method for Indoor Mobile Robot Navigation is introduced. This method is based on morphological operators and machine learning techniques and can be used in real time environments. The proposed method has two steps. At First, a new set of morphological operators is applied with a particular sequence to extract high contrast areas that have high probability of text existence. Using of morphological operators has many advantages such as: high computation speed, being invariant to several geometrical transformations like translation, rotations, and scaling, and being able to extract all areas containing text. After extracting text candidate regions, a set of nine features are extracted for accurate detection and deletion of the regions that don't have text. These features are descriptors for texture properties and are computed in real time. Then, we use a SVM classifier to detect the existence of text in the region. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.
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PDF链接：
https://arxiv.org/pdf/1709.09634