Stroke Detector and Structure Based Models for Character Recognition: A Comparative Study
Shi, Cun-Zhao; Gao, Song; Liu, Meng-Tao; Qi, Cheng-Zuo; Wang, Chun-Heng; Xiao, Bai-Hua; Shi Cunzhao
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2015-12-01
Volume24Issue:12Pages:4952-4964
SubtypeArticle
AbstractCharacters, which are man-made symbols composed of strokes arranged in a certain structure, could provide semantic information and play an indispensable role in our daily life. In this paper, we try to make use of the intrinsic characteristics of characters and explore the stroke and structure-based methods for character recognition. First, we introduce two existing part-based models to recognize characters by detecting the elastic strokelike parts. In order to utilize strokes of various scales, we propose to learn the discriminative multi-scale stroke detector-based representation (DMSDR) for characters. However, the part-based models and DMSDR need to manually label the parts or key points for training. In order to learn the discriminative stroke detectors automatically, we further propose the discriminative spatiality embedded dictionary learning-based representation (DSEDR) for character recognition. We make a comparative study of the performance of the tree-structured model (TSM), mixtures-of-parts TSM, DMSDR, and DSEDR for character recognition on three challenging scene character recognition (SCR) data sets as well as two handwritten digits recognition data sets. A series of experiments is done on these data sets with various experimental setup. The experimental results demonstrate the suitability of stroke detector-based models for recognizing characters with deformations and distortions, especially in the case of limited training samples.
KeywordCharacter Recognition Stroke Detector Structure Part-based Model Tree-structure Spatiality Embedded Codeword
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2015.2473105
WOS KeywordOBJECT RECOGNITION ; SCENE IMAGES ; ORIENTED GRADIENTS ; TEXT ; COOCCURRENCE ; SEGMENTATION ; EXTRACTION ; HISTOGRAM
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000362008200010
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10026
Collection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
Corresponding AuthorShi Cunzhao
AffiliationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shi, Cun-Zhao,Gao, Song,Liu, Meng-Tao,et al. Stroke Detector and Structure Based Models for Character Recognition: A Comparative Study[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(12):4952-4964.
APA Shi, Cun-Zhao.,Gao, Song.,Liu, Meng-Tao.,Qi, Cheng-Zuo.,Wang, Chun-Heng.,...&Shi Cunzhao.(2015).Stroke Detector and Structure Based Models for Character Recognition: A Comparative Study.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(12),4952-4964.
MLA Shi, Cun-Zhao,et al."Stroke Detector and Structure Based Models for Character Recognition: A Comparative Study".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.12(2015):4952-4964.
Files in This Item: Download All
File Name/Size DocType Version Access License
TIP-Stroke Detector (4543KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Shi, Cun-Zhao]'s Articles
[Gao, Song]'s Articles
[Liu, Meng-Tao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Shi, Cun-Zhao]'s Articles
[Gao, Song]'s Articles
[Liu, Meng-Tao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shi, Cun-Zhao]'s Articles
[Gao, Song]'s Articles
[Liu, Meng-Tao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: TIP-Stroke Detector and Strucsture based models for character recognition .pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.