Localizing Blurry and Low-Resolution Text in Natural Images.
Localizing Blurry and Low-Resolution Text in Natural Images.
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DOI：
10.1109/wacv.2011.5711546
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发表时间：
2011-02-10
期刊：
影响因子：
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通讯作者：
Coughlan JM
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文献类型：
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作者：
Sanketi P;Shen H;Coughlan JM
有越来越多的工作致力于解决在自然场景中出现的印刷文本区域的本地化问题，所有这些工作都集中在图像上，其中要本地化的文本被清晰地解析以供OCR阅读。本文介绍了一种文本本地化的替代方法，基于这样一个事实，即本地化文本通常是有用的，但太模糊或太小而无法阅读，原因有两个。首先，可以以比通常更粗糙的分辨率对图像进行抽取和处理，从而在执行OCR之前（如果需要，以全分辨率）更快地定位。第二，在诸如手机应用程序的实时应用程序中查找和阅读文本，文本最初可能是从较低分辨率的视频图像中获取的，在该视频图像中文本看起来太小而无法阅读;一旦确定了文本的存在和位置，更高的-分辨率的图像可以采取，以解决文本足够清楚地阅读它。我们证明了这种方法的概念证明，通过描述一种新的算法用于二值化图像并提取被称为“斑点”的候选文本特征，并将斑点分组和分类为文本和非文本类别。实验结果显示在各种图像中的文本解决太差，不能清楚地阅读，但仍然是我们的算法识别的文本。
There is a growing body of work addressing the problem of localizing printed text regions occurring in natural scenes, all of it focused on images in which the text to be localized is resolved clearly enough to be read by OCR. This paper introduces an alternative approach to text localization based on the fact that it is often useful to localize text that is identifiable as text but too blurry or small to be read, for two reasons. First, an image can be decimated and processed at a coarser resolution than usual, resulting in faster localization before OCR is performed (at full resolution, if needed). Second, in real-time applications such as a cell phone app to find and read text, text may initially be acquired from a lower-resolution video image in which it appears too small to be read; once the text’s presence and location have been established, a higher-resolution image can be taken in order to resolve the text clearly enough to read it. We demonstrate proof of concept of this approach by describing a novel algorithm for binarizing the image and extracting candidate text features, called “blobs,” and grouping and classifying the blobs into text and non-text categories. Experimental results are shown on a variety of images in which the text is resolved too poorly to be clearly read, but is still identifiable by our algorithm as text.