Degraded document image binarization using structural symmetry of strokes
Jia, Fuxi; Shi, Cunzhao; He, Kun; Wang, Chunheng; Xiao, Baihua
AbstractThis paper presents an effective approach for the local threshold binarization of degraded document images. We utilize the structural symmetric pixels (SSPs) to calculate the local threshold in neighborhood and the voting result of multiple thresholds will determine whether one pixel belongs to the foreground or not. The SSPs are defined as the pixels around strokes whose gradient magnitudes are large enough and orientations are symmetric opposite. The compensated gradient map is used to extract the SSP so as to weaken the influence of document degradations. To extract SSP candidates with large magnitudes and distinguish the faint characters and bleed-through background, we propose an adaptive global threshold selection algorithm. To further extract pixels with opposite orientations, an iterative stroke width estimation algorithm is applied to ensure the proper size of neighborhood used in orientation judgement. At last, we present a multiple threshold vote based framework to deal with some inaccurate detections of SSP. The experimental results on seven public document image binarization datasets show that our method is accurate and robust compared with many traditional and state-of-the-art document binarization approaches based on multiple evaluation measures. (C) 2017 Elsevier Ltd. All rights reserved.
KeywordDocument Image Binarization Structural Symmetry Of Strokes Local Threshold Stroke Width Estimation
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61601462 ; 61531019 ; 71621002)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000417547800018
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Cited Times:32[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
AffiliationUniv Chinese Acad Sci, Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jia, Fuxi,Shi, Cunzhao,He, Kun,et al. Degraded document image binarization using structural symmetry of strokes[J]. PATTERN RECOGNITION,2018,74(2018):225-240.
APA Jia, Fuxi,Shi, Cunzhao,He, Kun,Wang, Chunheng,&Xiao, Baihua.(2018).Degraded document image binarization using structural symmetry of strokes.PATTERN RECOGNITION,74(2018),225-240.
MLA Jia, Fuxi,et al."Degraded document image binarization using structural symmetry of strokes".PATTERN RECOGNITION 74.2018(2018):225-240.
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