In recent times, text detection in the wild has significantly raised its ability due to tremendous success of deep learning models. Applications of computer vision have emerged and got reshaped in a new way in this booming era of deep learning. In the last decade, research community has witnessed drastic changes in the area of text detection from natural scene images in terms of approach, coverage and performance due to huge advancement of deep neural network based models. In this paper, we present (1) a comprehensive review of deep learning approaches towards scene text detection, (2) suitable deep frameworks for this task followed by critical analysis, (3) a categorical study of publicly available scene image datasets and applicable standard evaluation protocols with their pros and cons, and (4) comparative results and analysis of reported methods. Moreover, based on this review and analysis, we precisely mention possible future scopes and thrust areas of deep learning approaches towards text detection from natural scene images on which upcoming researchers may focus.

中文翻译：

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场景文本检测的深度学习方法：全面回顾

最近，由于深度学习模型的巨大成功，野外文本检测的能力显着提高。在这个蓬勃发展的深度学习时代，计算机视觉的应用以一种新的方式出现并被重塑。在过去十年中，由于基于深度神经网络的模型的巨大进步，研究界见证了自然场景图像文本检测领域在方法、覆盖范围和性能方面的巨大变化。在本文中，我们提出 (1) 对场景文本检测的深度学习方法的全面回顾，(2) 适合此任务的深度框架，然后进行批判性分析，(3) 对公开可用的场景图像数据集和适用标准的分类研究评估协议及其优缺点，(4) 报告方法的比较结果和分析。此外，基于此回顾和分析，我们准确地提到了未来研究人员可能关注的从自然场景图像中进行文本检测的深度学习方法未来可能的范围和重点领域。