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
Source PublicationNeurocomputing
AbstractThis 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.

KeywordScene Text Detection Mser Cnn Crf Context Information Shape-specific Classifiers
Document Type期刊论文
Affiliation1.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
Recommended Citation
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.
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