深度卷积网络的自然场景文本检测研究综述

Survey of Natural Scene Text Detection Using the Deep Convolutional Networks

自然场景文本检测是从场景图像中获取文本信息的重要手段,但是仍旧面临背景复杂、文字种类丰富、排列方向多样、文本行组成复杂等因素的严峻挑战.研究检测精度高、通用性强、稳健性好的自然场景文本检测方法是目前计算机视觉领域的热点问题之一.并且,基于深度卷积网络的自然场景文本检测方法逐渐成为主流.因此,从自然场景文本检测技术的研究背景及主要挑战切入,根据骨干网络的不同将现有方法划分为基于VGG网络的文本检测方法、基于残差网络的文本检测方法和基于特征金字塔网络的文本检测方法,重点阐述各类方法的核心思想、技术优势及其不足;然后,总结自然场景文本检测的公共数据集,对代表性方法的检测性能进行客观比较;最后,梳理和总结自然场景文本检测技术的难点并展望其未来发展趋势.

Natural scene text detection is an important approach to obtain text information from scene images.Nevertheless,its techniques always face severe challenges from many factors,such as complex backgrounds,rich languages,diverse directions,and complex text line composition.Hence,it is one of the hot issues in the field of computer vision to study the natural scene text detection methods with high detection accuracy,strong versatility,and good robustness.Meanwhile,the methods based on the deep convolutional networks have become a mainstream.On introducing the background and challenges of text detection in natural scenes,this paper classifies the state-of-art methods into three categories according to different backbone networks,including the text detection methods based on VGG network,the methods based on ResNet network,as well as the ones based on FPN structure.The core ideas,technical advantages and shortcomings of various methods are expounded in detail.Subsequently,the public datasets are summarized for text detection in natural scenes,and an objective comparison is discussed among representative methods in terms of text detection performance.Finally,the difficulties of natural scene text detection are summarized.The future development trend is prospected of natural scene text detection based on the deep convolutional network as well.