Text Recognition on Images from Social Media

M. Akopyan, O.V. Belyaeva, T.P. Plechov, D. Turdakov
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引用次数: 10

Abstract

Text recognition problem has been studied many years. A few OCR engines exist, which successfully solve the problem for many languages. But these engines work well only with high quality scanned images. Social networks nowadays contain large number of images that need to analyze and recognize the text contained in them, but they have different quality: mixed text with images, poor quality images taken from camera of smartphone, etc. In this paper a text extraction pipeline is provided to address text extraction from various quality images collected form social media. Input images are categorized into different classes and then class specific preprocessing is applied to them (illumination improvement, text localization etc.). Then OCR engine used to recognize text. In the paper we present results of our experiments on dataset collected from social media.
基于社交媒体图像的文本识别
文本识别问题已经被研究了很多年。存在一些OCR引擎,它们成功地解决了许多语言的问题。但这些引擎只有在高质量的扫描图像上才能正常工作。现在的社交网络包含了大量的图片,需要对其中的文字进行分析和识别,但是这些图片的质量各不相同:文字和图片混合,智能手机相机拍摄的图像质量差等。本文提供了一个文本提取管道来解决从社交媒体上收集的各种高质量图像的文本提取问题。输入图像被分类为不同的类别,然后对其进行特定类别的预处理(照明改善,文本定位等)。然后用OCR引擎对文本进行识别。在本文中,我们展示了我们从社交媒体收集的数据集的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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