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首页> 外文会议>International conference on graphic and image processing >Text Extraction from Images in the Wild Using the Viola-Jones Algorithm
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Text Extraction from Images in the Wild Using the Viola-Jones Algorithm

机译：使用Viola-Jones算法从野外图像中提取文本

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Text Localization and extraction is an important issue in modern applications of computer vision. Applications such as reading and translating texts in the wild or from videos are among the many applications that can benefit results of this field. In this work, we adopt the well-known Viola-Jones algorithm to enable text extraction and localization from images in the wild. The Viola-Jones is an efficient, and a fast image-processing algorithm originally used for face detection. Based on some resemblance between text and face detection tasks in the wild, we have modified the viola-jones to detect regions of interest where text may be localized. In the proposed approach, some modification to the H AAR like features and a semi-automatic process of data set generating and manipulation were presented to train the algorithm. A process of sliding windows with different sizes have been used to scan the image for individual letters and letter clusters existence. A post processing step is used in order to combine the detected letters into words and to remove false positives. The novelty of the presented approach is using the strengths of a modified Viola-Jones algorithm to identify many different objects representing different letters and clusters of similar letters and later combine them into words of varying lengths. Impressive results were obtained on the ICDAR contest data sets.
机译：在计算机视觉的现代应用中，文本本地化和提取是一个重要的问题。诸如从野外或从视频中阅读和翻译文本之类的应用程序都是可以使该领域的结果受益的众多应用程序之一。在这项工作中，我们采用了著名的Viola-Jones算法，可以从野外图像中提取文本并进行本地化。 Viola-Jones是一种高效且快速的图像处理算法，最初用于面部检测。基于文本和野外面部检测任务之间的某些相似之处，我们对中提琴琼斯进行了修改，以检测可能定位了文本的感兴趣区域。在提出的方法中，提出了对H AAR类特征的一些修改以及数据集生成和处理的半自动过程，以训练该算法。已使用具有不同大小的滑动窗口的过程来扫描图像以查找单个字母和字母簇的存在。使用后处理步骤是为了将检测到的字母组合成单词并消除误报。所提出的方法的新颖性是利用改进的Viola-Jones算法的优势来识别代表不同字母和相似字母簇的许多不同对象，然后将它们组合成不同长度的单词。在ICDAR竞赛数据集上获得了令人印象深刻的结果。

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