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International Journal of Multimedia Information Retrieval
Erschienen in: International Journal of Multimedia Information Retrieval 3/2022

05.07.2022 | Trends and Surveys

Text detection, recognition, and script identification in natural scene images: a Review

verfasst von: Veronica Naosekpam, Nilkanta Sahu

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 3/2022

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Abstract

Text in natural scene images plays a vital role in scene understanding. It contains a rich and abundant amount of valuable semantic information useful in many applications such as analysis of products’ labels, autonomous driving, and blind navigation. Consequently, detection, recognition, and identification of scripts of texts present in scene images have recently received massive attention. This paper intends to walk through the advances on the mentioned topics, mainly focusing on the approaches proposed in the last 8–10 years. As per our knowledge, this paper is the first to provide a review on the scene text script identification. We also provide a clear and precise classification between conventional-, deep learning-, and hybrid-based methods, including their advantages and disadvantages. State-of-the-art evaluation metrics, benchmark datasets’ characteristics, and performances of the existing methods are also analyzed and discussed. Lastly, we present an insight into potential research directions to complete the review. We hope this review will provide a brief insight for the researchers into scene text understanding.
Vorheriger Artikel A literature review and perspectives in deepfakes: generation, detection, and applications
Nächster Artikel Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review

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Metadaten
Titel
Text detection, recognition, and script identification in natural scene images: a Review
verfasst von
Veronica Naosekpam
Nilkanta Sahu
Publikationsdatum
05.07.2022
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 3/2022
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-022-00243-8

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