2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] Tan G J, Sulong G, Rahim M S M. Writer identification: A comparative study across three world major languages. Forensic Science International, 2017, 279: 41-52 doi: 10.1016/j.forsciint.2017.07.034
    [2] Marti U V, Bunke H. The IAM-database: An English sentence database for off-line handwriting recognition. International Journal on Document Analysis and Recognition, 2002, 5: 39−46 doi: 10.1007/s100320200071
    [3] Bulacu M, Schomaker L, Vuurpijl L. Writer identification using edge-based directional features. In: Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR' 03). Edinburgh, UK: IEEE, 2003. 937−941
    [4] Nguyen H T, Nguyen C T, Ino T, Indurkhya B, Nakagawa M. Text-independent writer identification using convolutional neural network. Pattern Recognition Letters, 2019, 121: 104-112 doi: 10.1016/j.patrec.2018.07.022
    [5] He S, Schomaker L. Writer identification using curvature-free features. Pattern Recognition, 2017, 63: 451−464 doi: 10.1016/j.patcog.2016.09.044
    [6] Khan F A, Tahir M A, Khelifi F, Bouridane A, Almotaeryi R. Robust off-line text independent writer identification using bagged discrete cosine transform features. Expert Systems with Applications, 2017, 71: 404−415 doi: 10.1016/j.eswa.2016.11.012
    [7] 李昕, 丁晓青, 彭良瑞. 一种基于微结构特征的多文种文本无关笔迹鉴别方法. 自动化学报, 2009, 35(9): 1199−1208 doi: 10.3724/SP.J.1004.2009.01199

    Li Xin, Ding Xiao-Qing, Peng Liang-Rui. Writer identification based on improved microstructure features. Acta Automatica Sinica, 2009, 35(9): 1199−1208 doi: 10.3724/SP.J.1004.2009.01199
    [8] Bertolini D, Oliveira L S, Justino E, Sabourin R. Texture-based descriptors for writer identification and verification. Expert Systems with Applications, 2013, 40(6): 2069−2080 doi: 10.1016/j.eswa.2012.10.016
    [9] Fiel S, Sablatnig R. Writer retrieval and writer identification using local features. In: Proceedings of the 10th IAPR International Workshop on Document Analysis Systems. Gold Coast, QLD, Australia: IEEE, 2012. 145−149
    [10] Christlein V, Gropp M, Fiel S, Maier A. Unsupervised feature learning for writer identification and writer retrieval. In: Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR' 17). Kyoto, Japan: IEEE, 2017. 991−997
    [11] 陈使明, 王以松. 一种鲁棒的离线笔迹鉴别方法. 自动化学报, 2020, 46(1): 108−116

    Chen Shi-Ming, Wang Yi-Song. A robust off-line writer identification method. Acta Automatica Sinica, 2020, 46(1): 108−116
    [12] Christlein V, Bernecker D, Hönig F, Maier A, Angelopoulou E. Writer identification using GMM supervectors and exemplar-SVMs. Pattern Recognition, 2017, 63: 258−267 doi: 10.1016/j.patcog.2016.10.005
    [13] Siddiqi I, Vincent N. Text-independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recognition, 2010, 43(11): 3853−3865 doi: 10.1016/j.patcog.2010.05.019
    [14] Ghiasi G, Safabakhsh R. Offline text-independent writer identification using codebook and efficient code extraction methods. Image and Vision Computing, 2013, 31(5): 379−391 doi: 10.1016/j.imavis.2013.03.002
    [15] Khalifa E, Al-Maadeed S, Tahir M A, Bouridane A, Jamshed A. Off-line writer identification using an ensemble of grapheme codebook features. Pattern Recognition Letters, 2015, 59: 18−25 doi: 10.1016/j.patrec.2015.03.004
    [16] Hannad Y, Siddiqi I, El Kettani M E Y. Writer identification using texture descriptors of handwritten fragments. Expert Systems with Applications, 2016, 47: 14−22 doi: 10.1016/j.eswa.2015.11.002
    [17] Lowe D G. Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 2004, 60(2): 91−110 doi: 10.1023/B:VISI.0000029664.99615.94
    [18] Wu X Q, Tang Y B, Bu W. Offline text-independent writer identification based on scale invariant feature transformation. IEEE Transactions on Information Forensics and Security, 2014, 9(3): 526−536 doi: 10.1109/TIFS.2014.2301274
    [19] Xiong Y J, Wen Y, Wang P S P, Lu Y. Text-independent writer identification using SIFT descriptor and contour-directional feature. In: Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR' 15). Tunis, Tunisia: IEEE, 2015. 91−95
    [20] Khan F A, Khelifi F, Tahir M A, Bouridane A. Dissimilarity Gaussian mixture models for efficient offline handwritten text-independent identification using SIFT and RootSIFT descriptors. IEEE Transactions on Information Forensics and Security, 2019, 14(2): 289−303 doi: 10.1109/TIFS.2018.2850011
    [21] Christlein V, Bernecker D, Maier A, Angelopoulou E. Offline writer identification using convolutional neural network activation features. In: Proceedings of the 2015 German Conference on Pattern Recognition. Cham: Springer, 2015. 540−552
    [22] Fiel S, Sablatnig R. Writer identification and retrieval using a convolutional neural network. In: Proceedings of the 2015 International Conference on Computer Analysis of Images and Patterns. Cham: Springer, 2015. 26−37
    [23] He S, Schomaker L. Deep adaptive learning for writer identification based on single handwritten word images, \textit {Pattern Recognition}, 2019, 4(88): 64-74
    [24] DeGroot M H, Schervish M J. Probability and Statistics. Beijing: Higher Education Press, 2005. 324−332
    [25] 鄢煜尘, 李蔡媛, 邱益鸣, 陈庆虎. 基于因子分析的文本独立笔迹鉴定分类器. 武汉大学学报(工学版), 2018, 51(1): 91−94

    Yan Yu-Chen, Li Cai-Yuan, Qiu Yi-Ming, Chen Qing-Hu. Text-independent classifier for handwriting verification based on factor analysis. Engineering Journal of Wuhan University, 2018, 51(1): 91−94
    [26] Xiao J S, Tian H, Zhang Y Q, Zhou Y Q, Lei J F. Blind video denoising via texture-aware noise estimation. Computer Vision and Image Understanding, 2018, 169: 1−13
    [27] Hadjadji B, Chibani Y. Two combination stages of clustered one-class classifiers for writer identification from text fragments. Pattern Recognition, 2018, 82: 147−162
    [28] Chahi A, Merabet Y EI, Ruichek Y, Touahni R. An effective and conceptually simple feature representation for off-line text-independent writer identification. Expert Systems with Applications, 2019, 123: 357−376
  • 加载中
图(10) / 表(5)
计量
  • 文章访问数:  121
  • HTML全文浏览量:  63
  • PDF下载量:  49
  • 被引次数: 0
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

目录

    /

    返回文章
    返回