Review of Natural Scene Text Detection Based on Deep Learning
Natural scene text detection technology based on deep learning has become a crucial research focal point in the fields of computer vision and natural language processing.Not only does it possess a wide range of potential applications but also serves as a new platform for researchers to explore neural network models and algorithms.First,this study introduces the relevant concepts,research background,and current developments in natural scene text detection technology.Subsequently,an analysis of recent deep learning-based text detection methods is performed,categorizing them into four classes:detection boxes-,segmentation-,detection-boxes and segmentation-based,and others.The fundamental concepts and main algorithmic processes of classical and mainstream methods within these four categories are elaborated,summarizing the usage mechanisms,applicable scenarios,advantages,disadvantages,simulation experimental results,and environment settings of different methods,while clarifying their interrelationships.Thereafter,common public datasets and performance evaluation methods for natural scene text detection are introduced.Finally,the major challenges facing current deep learning-based natural scene text detection technology are outlined,and future development directions are discussed.
deep learningcomputer visionnatural scene texttext detectionmulti-directional text detectionmulti-scale text detection
国家科技期刊平台
NSTL
万方数据
