CRF based text detection for natural scene images using convolutional

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 影像分析与机器视觉团队

	CRF based text detection for natural scene images using convolutional
	Wang YN(王燕娜)1,2 ; Shi,Cunzhao1 ; Baihua Xiao1 ; Chunheng Wang1 ; Chengzuo Qi1,2
刊名	Neurocomputing
	2018
期号	295 页码:46-58
关键词	Scene Text Detection Mser Cnn Crf Context Information Shape-specific Classifiers
英文摘要	This paper presents a novel scene text detection method based on conditional random field (CRF) framework. We estimate the confidence of Maximally Stable Extremal Region (MSER) being text by leveraging convolutional neural network (CNN) to define the unary cost item. In addition, we establish the neighboring interactions for MSERs using four different features including color, shape, stroke and spatial features to define the pairwise cost item. Considering the special layout of texts appearing in natural scene images, we employ context information to recover missing text MSER candidates. Furthermore, text MSERs are grouped into candidate text lines which are verified with shape-specific classifiers by integrating gray and binary features. Experimental results on four public benchmark datasets show that the proposed method achieves the comparable performance.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21036]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_影像分析与机器视觉团队
作者单位	1.The State Key Laboratory of Management and Control for Complex Systems,Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Wang YN,Shi,Cunzhao,Baihua Xiao,et al. CRF based text detection for natural scene images using convolutional[J]. Neurocomputing,2018(295):46-58.
APA	Wang YN,Shi,Cunzhao,Baihua Xiao,Chunheng Wang,&Chengzuo Qi.(2018).CRF based text detection for natural scene images using convolutional.Neurocomputing(295),46-58.
MLA	Wang YN,et al."CRF based text detection for natural scene images using convolutional".Neurocomputing .295(2018):46-58.