Degraded document image binarization using structural symmetry of strokes
Jia, Fuxi; Shi, Cunzhao; He, Kun; Wang, Chunheng; Xiao, Baihua
2018-02-01
发表期刊PATTERN RECOGNITION
卷号74期号:2018页码:225-240
文章类型Article
摘要This 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.
关键词Document Image Binarization Structural Symmetry Of Strokes Local Threshold Stroke Width Estimation
WOS标题词Science & Technology ; Technology
DOI10.1016/j.patcog.2017.09.032
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61601462 ; 61531019 ; 71621002)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000417547800018
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19604
专题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
作者单位Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式
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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