  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 field, the
objectives of this survey are as follows: first, offering the reader not only a
review on the recent advancement in scene text detection and recognition, but
also presenting the results of conducting extensive experiments using a unified
evaluation framework that assesses pre-trained models of the selected methods
on challenging cases, and applies the same evaluation criteria on these
techniques. Second, identifying several existing challenges for detecting or
recognizing text in the wild images, namely, in-plane-rotation, multi-oriented
and multi-resolution text, perspective distortion, illumination reflection,
partial occlusion, complex fonts, and special characters. Finally, the paper
also presents insight into the potential research directions in this field to
address some of the mentioned challenges that are still encountering scene text
detection and recognition techniques.
