基于深度学习的场景文字检测研究进展

doi:10.3969/j.issn.1000-5641.2018.05.001

摘要/Abstract

摘要： 在大数据驱动应用的背景下，随着计算机硬件性能的提高，基于深度学习的目标检测和图像分割算法冲破了传统算法的瓶颈，成为当前计算机视觉领域的主流算法.而场景文字检测任务受到目标检测和图像分割算法发展的影响，近年来也有了极大的突破.这篇综述的目的主要有3个方面：介绍近5年场景文字检测工作进展；比较分析先进算法的优点及不足；总结该领域相关的基准数据集和评价方法.

关键词: 文字检测, 深度学习, 自然场景, 目标检测, 图像分割

Abstract: With improvements in computer hardware performance, object detection, and image segmentation algorithms (based on deep learning) have broken the bottlenecks posed by traditional algorithms in big data-driven applications and become the mainstream algorithms in the field of computer vision. In this context, scene text detection algorithms have made great breakthroughs in recent years. The objectives of this survey are three-fold:introduce the progress of scene text detection over the past 5 years, compare and analyze the advantages and limitations of advanced algorithms, and summarize the relevant benchmark datasets and evaluation methods in the field.

Key words: text detection, deep learning, natural scene, object detection, image segmentation

中图分类号:

TP391

余若男, 黄定江, 董启文. 基于深度学习的场景文字检测研究进展[J]. 华东师范大学学报(自然科学版), 2018, 2018(5): 1-16.

YU Ruo-nan, HUANG Ding-jiang, DONG Qi-wen. Survey on scene text detection based on deep learning[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(5): 1-16.

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