A Comprehensive Review on Text Detection and Recognition in Scene Images

Abstract

Detecting and recognizing text in natural scene images and videos is vital for several real-world applications, such as in the analysis of Crime scene CCTV footage, sports videos, and autonomous driving, to name a few. Therefore, one can expect several challenges, namely, arbitrarily oriented and shaped Text detection and identification in movies and natural environments. Many methods have been developed in the past to address these challenges, including advanced deep-learning models and transformers. Due to several methods available in the literature, it is not so easy to understand the open challenges, applications, directions, scope, limitations, and weaknesses of the methods. Therefore, there is a need to write a survey/review to highlight and discuss the strengths and weaknesses of the developed methods. This survey/review presents different categories of work and discusses their importance, limitations, new challenges, applications, and, finally, directions such that readers can choose appropriate methods and directions to carry out research work in the field of text detection/recognition in the natural scene and videos.

Received: 4 March 2024 | Revised: 24 September 2024 | Accepted: 25 September 2024

Conflicts of Interest

Palaiahnakote Shivakumara is the Editor-in-Chief and Umapada Pal is an Advisory Board Member for Artificial Intelligence and Applications, and were not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Arnab Halder: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft. Palaiahnakote Shivakumara: Methodology, Validation, Investigation, Writing - original draft, Supervision. Umapada Pal: Writing - review & editing, Supervision. Michael Blumenstein: Visualization.