With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.
翻译:随着深层学习的兴起和发展,计算机的视野发生了巨大的变化和改变。作为计算机视野的一个重要研究领域,现场文本的探测和识别不可避免地受到这一革命浪潮的影响,从而进入了深层学习的时代。近年来,社区在思维、方法和业绩方面取得了巨大进步。这项调查旨在总结和分析深层学习时代现场文本探测和识别的重大变化和重大进展。我们通过这一文章致力于:(1) 介绍新的见解和想法;(2) 突出最近的技术和基准;(3) 展望未来趋势。具体地说,我们将强调深层学习带来的巨大差异,以及仍然存在的巨大挑战。我们期望这一审查文件将成为该领域研究人员的参考书。相关的资源也将在我们的Github文献库中收集和汇编:https://github.com/Jyoouhou/ScenTextPappers。