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基于混合码本与因子分析的文本独立笔迹鉴别

阿依夏木 ·力提甫 鄢煜尘 肖进胜 江昊 姚渭箐

阿依夏木 ·力提甫, 鄢煜尘, 肖进胜, 江昊, 姚渭箐. 基于混合码本与因子分析的文本独立笔迹鉴别. 自动化学报, 2021, 47(9): 2276−2284 doi: 10.16383/j.aas.c190121
引用本文: 阿依夏木 ·力提甫, 鄢煜尘, 肖进胜, 江昊, 姚渭箐. 基于混合码本与因子分析的文本独立笔迹鉴别. 自动化学报, 2021, 47(9): 2276−2284 doi: 10.16383/j.aas.c190121
Ayixiamu · Litifu, Yan Yu-Chen, Xiao Jin-Sheng, Jiang Hao, Yao Wei-Qing. Text-independent writer identification based on hybrid codebook and factor analysis. Acta Automatica Sinica, 2021, 47(9): 2276−2284 doi: 10.16383/j.aas.c190121
Citation: Ayixiamu · Litifu, Yan Yu-Chen, Xiao Jin-Sheng, Jiang Hao, Yao Wei-Qing. Text-independent writer identification based on hybrid codebook and factor analysis. Acta Automatica Sinica, 2021, 47(9): 2276−2284 doi: 10.16383/j.aas.c190121

基于混合码本与因子分析的文本独立笔迹鉴别

doi: 10.16383/j.aas.c190121
基金项目: 新疆维吾尔自治区高校科研计划自然科学青年项目(XJUDU2019Y032), 新疆师范大学重点实验室招标课题(XJNUSYS092018A02)资助
详细信息
    作者简介:

    阿依夏木 ·力提甫:武汉大学电子信息学院博士研究生. 2012年获得南京理工大学电光学院工学硕士学位. 主要研究方向为图像处理与模式识别. E-mail: Ayixia@whu.edu.cn

    鄢煜尘:2009年于武汉大学获工学博士学位. 主要研究方向为图像处理与模式识别. E-mail: yyc@whu.edu.cn

    肖进胜:武汉大学电子信息学院副教授. 2001年于武汉大学获理学博士学位. 主要研究方向为视频图像处理, 计算机视觉. E-mail: xiaojs@whu.edu.cn

    江昊:博士, 武汉大学电子信息学院教授. 主要研究方向为移动自组网络, 移动大数据, 数据挖掘. 本文通信作者. E-mail: jh@whu.edu.cn

    姚渭箐:国网湖北省电力有限公司信息通信公司工程师. 2017年于武汉大学获工学博士学位. 主要研究方向为网络通信, 视频图像处理. E-mail: ywq1005@whu.edu.cn

Text-independent Writer Identification Based on Hybrid Codebook and Factor Analysis

Funds: Supported by Xinjiang Uyghur Autonomous Region University Scientific Research Program Natural Science Youth Project (XJUDU2019Y032) and Tender Subject for Key Laboratory Project of Xinjiang Normal University (XJNUSYS092018A02)
More Information
    Author Bio:

    Ayixiamu · LITIFU Ph.D. candidate at the Electronic Information School, Wuhan University. She received her master degree from Nanjing University of Technology in 2012. Her research interest covers image processing and pattern recognition

    YAN Yu-Chen Received his Ph.D. degree in engineering from the Electronic Information School, Wuhan University in 2009. His research interest covers image processing and pattern recognition

    XIAO Jin-Sheng Associate professor at the Electronic Information School, Wuhan University. His research interest covers video and image processing, and computer vision

    JIANG Hao Ph.D., professor at the Electronic Information School, Wuhan University. His research interest covers mobile ad hoc network, mobile big data, and data mining. Corresponding author of this paper

    YAO Wei-Qing Engineer at the Information Telecommuni-cation Company State Grid Hubei Electric Power Co., Ltd. She received her Ph.D. degree in engineeringfrom the Electronic Information School, Wuhan Universityin 2017. Her research interest covers network communication, video and image processing

  • 摘要: 针对已有的笔迹鉴别方法对笔迹版式的要求比较严格、训练过程耗时、对内容不受限制的小样本数据情况下鉴别性能较低等问题, 提出了基于混合码本与因子分析的文本独立笔迹鉴别算法. 该算法提取写作时常用的子图像, 并用描述符标注“代码”建立“码本”. 在特征提取层, 分别采用加权的方向指数直方图法和距离变换法, 对于具有相同描述符的“代码”计算特征距离. 把影响特征距离的因素分为书写因子和字符因子, 对码本中的每个书写模式进行双因子方差分析. 在IAM和Firemaker这两个标准数据集上的实验结果证明, 相比目前国内外的先进已有方法, 本文提出的算法在精度和速度方面有一定的优势, 具有一定的推广价值, 适合处理多语种的笔迹鉴别问题.
  • 图  1  混合码本生成与因子分析的总流程图

    Fig.  1  The overall flow chart of proposed method

    图  2  子图像的提取方法

    Fig.  2  Sub-image extraction method

    图  3  码本的生成过程

    Fig.  3  The generation process of codebook

    图  4  单词“the” 的加权方向指数直方图

    Fig.  4  Weighted direction index histogram of “the”

    图  5  数字 “6” 的距离变换

    Fig.  5  Distance transformation of number “6”

    图  6  方差分析笔迹图像

    Fig.  6  Handwriting image of variance analysis

    图  7  $\alpha $$\substack{ {{F}}_\alpha}$(10, 2090)和$\substack{{{F}}_\alpha }$(209, 2090)之间的关系

    Fig.  7  The relationship between $\alpha $ and $\substack{{{F}}_\alpha}$(10, 2090) and $\substack{{{F}}_\alpha}$(209, 2090)

    图  8  子图像数量与鉴别准确率之间的关系

    Fig.  8  Relationship between number of codes and identification accuracy

    图  9  书写人数量与鉴别准确率之间的关系

    Fig.  9  Identification accuracy with different number of writers

    图  10  维吾尔文2016数据集的性能示意图

    Fig.  10  Performance on Uyghur2016 dataset

    表  1  双因子方差分析(TW-ANOVA)指示表

    Table  1  Two way analysis of variance instruction table

    方差来源平方和自由度均方F比
    书写因子$S_A$$N-1$$S_A/({N-1})$$F_A$
    字符因子$S_B$$M-1$$S_B/({M-1})$$F_B$
    误差$S_E$$(N-1)(M-1)$$S_E/({(N-1)(M-1)})$
    总和$S_T$$MN-1$
    下载: 导出CSV

    表  2  加权方向指数直方图法/距离变换法的TW-ANOVA结果

    Table  2  Results of WDIH/DT method of TW-ANOVA

    方差来源平方和自由度均方F比
    书写因子1.76/4.23100.176/0.42324.11/34.67
    字符因子28.14/31.522090.1346/0.149518.44/12.25
    误差15.23/25.612 0900.0073/0.0122
    总和45.13/61.362 309
    下载: 导出CSV

    表  3  各种方法在${\rm{Firemaker}}$数据集上的性能对比(%)

    Table  3  Performance comparison on Firemaker (%)

    评估标准TOP-1TOP-10
    Ghiasi (2013)[14]89.298.6
    Wu (2014)[18]92.498.8
    He (2017)[5]86.296.6
    Nguyen (2019)[4]92.3897.67
    本文方法94.498.8
    下载: 导出CSV

    表  4  各种方法在${\rm{ IAM }}$数据集上的性能对比(%)

    Table  4  Performance comparison on IAM dataset (%)

    评估标准TOP-1TOP-10
    Siddiqi (2010)[13]91.097.0
    Ghiasi (2013)[14]93.797.7
    Bertolini (2013)[8]88.3
    Wu (2014)[18]98.599.5
    Khalifa (2015)[15]92.0
    Hannad (2016)[16]89.5496.77
    Khan (2017)[6]97.2
    He (2017)[5]89.996.9
    Nguyen (2019)[4]90.1297.82
    Hadjadji (2018)[27]94.51
    Chahi (2019)[28]88.3
    本文方法95.6999.69
    下载: 导出CSV

    表  5  在三个数据集上的性能对比(%)

    Table  5  Performance comparisons on three datasets (%)

    评估标准TOP-1TOP-10
    维吾尔文 2016 数据集100100
    IAM 数据集95.6999.69
    Firemaker 数据集94.498.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-01
  • 录用日期:  2019-06-02
  • 刊出日期:  2021-10-13

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