Mining text from natural scene and video images: A survey

Shivakumara, Palaiahnakote; Alaei, Alireza; Pal, Umapada

Abstract

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.

Citation

Shivakumara, P., Alaei, A., & Pal, U. (2021). Mining text from natural scene and video images: A survey. Data Mining and Knowledge Discovery, 11(6), https://doi.org/10.1002/widm.1428

• Journal Article Type: Article
• Acceptance Date: Jul 26, 2021
• Publication Date: 2021-11
• Deposit Date: Feb 2, 2024
• Journal: WIREs Data Mining and Knowledge Discovery
• Print ISSN: 1384-5810
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 6
• DOI: https://doi.org/10.1002/widm.1428