Mining text from natural scene and video images: A survey,WIREs Data Mining and Knowledge Discovery

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Mining text from natural scene and video images: A survey
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-08-24 , DOI: 10.1002/widm.1428
Palaiahnakote Shivakumara ₁ , Alireza Alaei ₂ , Umapada Pal ₃

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In computer terminology, mining is considered as extracting meaningful information or knowledge from a large amount of data/information using computers. The meaningful information can be extracted from normal text, and images obtained from different resources, such as natural scene images, video, and documents by deriving semantics from text and content of the images. Although there are many pieces of work on text/data mining and several survey/review papers are published in the literature, to the best of our knowledge there is no survey paper on mining textual information from the natural scene, video, and document images considering word spotting techniques. In this article, we, therefore, provide a comprehensive review of both the non-spotting and spotting based mining techniques. The mining approaches are categorized as feature, learning and hybrid-based methods to analyze the strengths and limitations of the models of each category. In addition, it also discusses the usefulness of the methods according to different situations and applications. Furthermore, based on the review of different mining approaches, this article identifies the limitations of the existing methods and suggests new applications and future directions to continue the research in multiple directions. We believe such a review article will be useful to the researchers to quickly become familiar with the state-of-the-art information and progresses made toward mining textual information from natural scene and video images.

中文翻译：

从自然场景和视频图像中挖掘文本：一项调查

在计算机术语中，挖掘被认为是使用计算机从大量数据/信息中提取有意义的信息或知识。通过从文本和图像内容中推导出语义，可以从普通文本和从自然场景图像、视频和文档等不同资源获得的图像中提取有意义的信息。尽管在文本/数据挖掘方面有很多工作，并且文献中发表了几篇调查/评论论文，但据我们所知，还没有关于从自然场景、视频和文档图像中挖掘文本信息的调查论文考虑到单词识别技术。因此，在本文中，我们将对基于非斑点和基于斑点的挖掘技术进行全面回顾。挖掘方法被归类为特征，学习和基于混合的方法来分析每个类别模型的优势和局限性。此外，还根据不同的情况和应用讨论了这些方法的实用性。此外，基于对不同挖掘方法的回顾，本文确定了现有方法的局限性，并提出了新的应用和未来方向，以在多个方向上继续研究。我们相信这样的评论文章将有助于研究人员快速熟悉最先进的信息以及在从自然场景和视频图像中挖掘文本信息方面取得的进展。它还根据不同的情况和应用讨论了这些方法的有用性。此外，基于对不同挖掘方法的回顾，本文确定了现有方法的局限性，并提出了新的应用和未来方向，以在多个方向上继续研究。我们相信这样的评论文章将有助于研究人员快速熟悉最先进的信息以及在从自然场景和视频图像中挖掘文本信息方面取得的进展。它还根据不同的情况和应用讨论了这些方法的有用性。此外，基于对不同挖掘方法的回顾，本文确定了现有方法的局限性，并提出了新的应用和未来方向，以在多个方向上继续研究。我们相信这样的评论文章将有助于研究人员快速熟悉最先进的信息以及在从自然场景和视频图像中挖掘文本信息方面取得的进展。

更新日期：2021-10-15

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