Text Detection and Recognition in the Wild: A Review
Zobeir Raisi, Mohamed A. Naiel, Paul Fieguth, Steven Wardell, and John, Zelek

TL;DR
This survey reviews recent advances in scene text detection and recognition, evaluates pre-trained models on challenging real-world images, and discusses ongoing challenges and future research directions in the field.
Contribution
It provides a comprehensive review combined with extensive experimental evaluation of methods on challenging datasets, highlighting current challenges and future research directions.
Findings
Deep learning methods achieve high accuracy on benchmark datasets.
Existing models struggle with unseen data and complex environmental conditions.
The paper identifies key challenges like rotation, occlusion, and illumination in wild images.
Abstract
Detection and recognition of text in natural images are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Vehicle License Plate Recognition · Advanced Image and Video Retrieval Techniques
