计算机应用与软件2018，Vol.35Issue(2)：218-223,241,7.DOI:10.3969/j.issn.1000-386x.2018.02.040
基于支持向量机的低质量文档图像二值化
LOW QUALITY DOCUMENT IMAGE BINARIZATION BASED ON SUPPORT VECTOR MACHINE
摘要
Abstract
In view of the influence of ink infiltration,page stains,background texture or uneven illumination of low-quality document images,a low-quality document image binarization method based on Support Vector Machine(SVM)is proposed.First,the method segmented the image into blocks,and enhanced local contrast of each image block.Then, we classified those image blocks into three categories via SVM classifier.Three different thresholding were respectively used for the three categories, which achieved a rough segmentation.Finally, we determined the neighborhood window size by means of stroke width estimation, so as to achieve a precise segmentation of the foreground text and document background.Experimental results showed that the proposed algorithm had obvious advantages over other binarization methods in terms of binary image quality and various evaluation parameters.关键词
低质量文档图像二值化/支持向量机(SVM)/局部对比度/笔画宽度估计Key words
Low quality document image binarization/Support vector machine(SVM)/Local contrast/Stroke width estimation分类
信息技术与安全科学引用本文复制引用
熊炜,徐晶晶,赵诗云,王改华,刘敏,赵楠,刘聪..基于支持向量机的低质量文档图像二值化[J].计算机应用与软件,2018,35(2):218-223,241,7.基金项目
国家自然科学基金青年科学基金项目(61501178,61601177) （61501178,61601177）
湖北省教育厅科学技术研究计划重点项目(D20161404). （D20161404）
