A cascaded method for text detection in natural scene images
Zheng, Yang1; Li, Qing1; Liu, Jie2; Liu, Heping1; Li, Gen2; Zhang, Shuwu2
Source PublicationNEUROCOMPUTING
AbstractIn this paper, a novel image operator is proposed to detect and locate text in scene images. To achieve a high recall of character detection, extremal regions are detected as character candidates. Two classifiers are trained to identify characters, and a recursive local search algorithm is proposed to extract characters that are wrongly identified by the classifiers. An efficient pruning method, which combines component trees and recognition results, is proposed to prune repeating components. A cascaded method combines text line entropy with a Convolutional Neural Network model. It is used to verify text candidates, which reduces the number of non -text regions. The proposed technique is test on three public datasets, i.e. ICDAR2011 dataset, ICDAR2013 dataset and ICDAR2015 dataset. The experimental results show that our approach achieves state-of-the-art performance. (C) 2017 Elsevier B.V. All rights reserved.
KeywordExtremal Regions Recursive Local Search Text Line Entropy Convolutional Neural Network
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000397372100029
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Document Type期刊论文
Affiliation1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, 30 Xueyuan Rd, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zheng, Yang,Li, Qing,Liu, Jie,et al. A cascaded method for text detection in natural scene images[J]. NEUROCOMPUTING,2017,238(238):307-315.
APA Zheng, Yang,Li, Qing,Liu, Jie,Liu, Heping,Li, Gen,&Zhang, Shuwu.(2017).A cascaded method for text detection in natural scene images.NEUROCOMPUTING,238(238),307-315.
MLA Zheng, Yang,et al."A cascaded method for text detection in natural scene images".NEUROCOMPUTING 238.238(2017):307-315.
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