基于改进的多通道Gabor小波变换的笔迹鉴别
|
摘要
本论文以笔迹书写人身份鉴别问题为背景，研究基于改进的多通道Gabor小波变换的笔迹鉴别问题，建立了基于该算法的笔迹身份鉴别系统。论文工作涉及：笔迹图像的获取，笔迹图像的预处理，笔迹图像的纹理特征提取，笔迹图像的分类匹配，以及基于改进的多通道Gabor小波变换的笔迹鉴别系统的构造和实现。论文取得的主要研究成果如下：
(1) 论文提出了一种改进的盐和胡椒滤波器。原算法只运用于对黑白二值图像去除噪声，而灰度图带有更多的反映书写人习惯的特征信息。改进的盐和胡椒滤波器可应用于灰度笔迹图像，对于分布广泛且不均匀的噪声点的去除有较好的效果。
(2) 由于笔迹纸张中的网格线不代表书写人的笔迹风格，且会影响特征提取的准确性，将这些网格线与笔迹文字分离并去除是预处理中的重要步骤。为此本论文提出了基于中文笔迹中使用的信纸、稿纸等带有特殊格线的纸张的去除背景的方法，实验证明了此方法的有效性。
(3) 为了去除行间距、字间距、不同行高等不带书写人笔迹风格而影响特征提取准确性的因素，论文对笔迹图像的范化问题进行了研究，提出了一种将水平投影法、垂直投影法和字块拼接法结合的笔迹字块归一化的方法，使归一化操作一次完成，同时保证了笔迹文本内容的完整性。
(4) 论文对Gabor小波变换算法进行了改进，提出了适合笔迹特征提取的纹理分析算法。该算法与笔迹所写内容无关，避免了对笔迹图像文字进行分割的操作，符合鉴别笔迹的习惯。该算法可记录下每一个通道的书写人笔迹风格的特征向量——均值和方差，它们记录下了每个通道笔迹图像纹理特征的重要信息。论文选取40个通道的80维向量记录在该笔迹书写人的笔迹模式库中。
(5) 论文对笔迹图像分类器进行了设计，通过加权欧式据立法和k-近邻算法对笔迹样本进行分类匹配，将训练后的80维特征向量以纯文本格式保存于不同的笔迹模式库中。其优点为数据存储量小、便于管理，辨识速度快。
论文在Windows 98环境下用Microsoft Visual C＋＋6.0编程完成系统。采用多线程方式，使用50个人的不同手写中文笔迹进行实验，识别率达到97.6％。同时，论文还提出了多生物特征的身份鉴别融合系统的设计方案；将基于改进的多通道Gabor小波变换的笔迹鉴别系统用在对印刷体汉字的识别，取得了较好的效果，平均识别率达到99％。因此，该离线的基于笔迹的身份鉴别系统具有一定的实用性。
This thesis is based on the Writer Identification (WI) issue. The improved multi-channel Gabor wavelet technique is solved in the handwriting identification issue. Based on this method, the system is also set up to fulfill writer identification. The main contents of this paper include: get the handwriting images, preprocess the handwriting images, extract the textural characters from the handwriting images, classify the handwriting images, construct and realize the writer identification system based on the improved multi-channel Gabor wavelet technique. The achievements of this thesis are as follows.
(1) The advanced salt and pepper filter is used in the thesis. The original method just can be used in removing the noise from binary images. The gray images contain much information about writer habit. So the advanced salt and pepper filter is applied in the gray handwriting images. The best result is achieved in these handwriting images, which have the broad and pockety noise.
(2) The shading of paper has a lot of grids or lines. These are not writers' handwriting styles and even influence the veracity of texture extraction. Therefore, the important preprocessing step is to separate the background from handwriting images. The experiments prove this method can remove the background effectively such as the grids of French folio paper and the lines of letter paper.
(3) Due to the handwriting image may contain lines of different height and the different spacing between words and lines. These factors will influence the veracity of texture extraction. So I advance a method to normalize the handwriting image, which combine the Horizonal Projection Profile method, Vertical Projection Profile method and padding method. This method completes the normalized processing once and guarantee the consistence of contain sequence and the concordance of paragraph.
(4) The paper advances Gabor wavelet technique and proposes the textural arithmetic to adapt to hanwriting. This arithmetic is a text independent method, so we need not segmente the handwriting text. The arithmetic records the mean and standard deviation of each channel. These are the important information of textural characters. We choose 80 vectors of 40 channels and save them in handwriting database.
(5) We use a weighted Euclidean Distance (WED) classifier and k-nearest neighbor (k-NN) classifier to fulfill the identification task. The eigenvectors are recorded in different handwriting database with text format.
The system is programmed with Microsoft Visual C++ 6.0 under Windows 98 and adopts multithreading mode. Experiments are made using Chinese handwriting from SO different people and very promising results (97.6%) were achieved. Otherwise, the thesis addresses the concept of multi-biometric personal identification system, hi addition, the promising results (99%) were also achieved in print font recognition. So the off-line Writer Identification system has definite practicability.
(1) 论文提出了一种改进的盐和胡椒滤波器。原算法只运用于对黑白二值图像去除噪声，而灰度图带有更多的反映书写人习惯的特征信息。改进的盐和胡椒滤波器可应用于灰度笔迹图像，对于分布广泛且不均匀的噪声点的去除有较好的效果。
(2) 由于笔迹纸张中的网格线不代表书写人的笔迹风格，且会影响特征提取的准确性，将这些网格线与笔迹文字分离并去除是预处理中的重要步骤。为此本论文提出了基于中文笔迹中使用的信纸、稿纸等带有特殊格线的纸张的去除背景的方法，实验证明了此方法的有效性。
(3) 为了去除行间距、字间距、不同行高等不带书写人笔迹风格而影响特征提取准确性的因素，论文对笔迹图像的范化问题进行了研究，提出了一种将水平投影法、垂直投影法和字块拼接法结合的笔迹字块归一化的方法，使归一化操作一次完成，同时保证了笔迹文本内容的完整性。
(4) 论文对Gabor小波变换算法进行了改进，提出了适合笔迹特征提取的纹理分析算法。该算法与笔迹所写内容无关，避免了对笔迹图像文字进行分割的操作，符合鉴别笔迹的习惯。该算法可记录下每一个通道的书写人笔迹风格的特征向量——均值和方差，它们记录下了每个通道笔迹图像纹理特征的重要信息。论文选取40个通道的80维向量记录在该笔迹书写人的笔迹模式库中。
(5) 论文对笔迹图像分类器进行了设计，通过加权欧式据立法和k-近邻算法对笔迹样本进行分类匹配，将训练后的80维特征向量以纯文本格式保存于不同的笔迹模式库中。其优点为数据存储量小、便于管理，辨识速度快。
论文在Windows 98环境下用Microsoft Visual C＋＋6.0编程完成系统。采用多线程方式，使用50个人的不同手写中文笔迹进行实验，识别率达到97.6％。同时，论文还提出了多生物特征的身份鉴别融合系统的设计方案；将基于改进的多通道Gabor小波变换的笔迹鉴别系统用在对印刷体汉字的识别，取得了较好的效果，平均识别率达到99％。因此，该离线的基于笔迹的身份鉴别系统具有一定的实用性。
This thesis is based on the Writer Identification (WI) issue. The improved multi-channel Gabor wavelet technique is solved in the handwriting identification issue. Based on this method, the system is also set up to fulfill writer identification. The main contents of this paper include: get the handwriting images, preprocess the handwriting images, extract the textural characters from the handwriting images, classify the handwriting images, construct and realize the writer identification system based on the improved multi-channel Gabor wavelet technique. The achievements of this thesis are as follows.
(1) The advanced salt and pepper filter is used in the thesis. The original method just can be used in removing the noise from binary images. The gray images contain much information about writer habit. So the advanced salt and pepper filter is applied in the gray handwriting images. The best result is achieved in these handwriting images, which have the broad and pockety noise.
(2) The shading of paper has a lot of grids or lines. These are not writers' handwriting styles and even influence the veracity of texture extraction. Therefore, the important preprocessing step is to separate the background from handwriting images. The experiments prove this method can remove the background effectively such as the grids of French folio paper and the lines of letter paper.
(3) Due to the handwriting image may contain lines of different height and the different spacing between words and lines. These factors will influence the veracity of texture extraction. So I advance a method to normalize the handwriting image, which combine the Horizonal Projection Profile method, Vertical Projection Profile method and padding method. This method completes the normalized processing once and guarantee the consistence of contain sequence and the concordance of paragraph.
(4) The paper advances Gabor wavelet technique and proposes the textural arithmetic to adapt to hanwriting. This arithmetic is a text independent method, so we need not segmente the handwriting text. The arithmetic records the mean and standard deviation of each channel. These are the important information of textural characters. We choose 80 vectors of 40 channels and save them in handwriting database.
(5) We use a weighted Euclidean Distance (WED) classifier and k-nearest neighbor (k-NN) classifier to fulfill the identification task. The eigenvectors are recorded in different handwriting database with text format.
The system is programmed with Microsoft Visual C++ 6.0 under Windows 98 and adopts multithreading mode. Experiments are made using Chinese handwriting from SO different people and very promising results (97.6%) were achieved. Otherwise, the thesis addresses the concept of multi-biometric personal identification system, hi addition, the promising results (99%) were also achieved in print font recognition. So the off-line Writer Identification system has definite practicability.
引文
[1]A. K Jain, R. Bolle and S. Pankanti, Biometrics: Personal Identification in Networked Society,Kluwer academic publisher, 1999.
[2]R.Clarke, Human Identification in Information Systems: Management Challenges and Public Policy Issues,Information Technology & Prople, vol.7,no.4,1994, pp.6-37.
[3]F.J. Prokoski, R. B. Riedel, and J. S.Coffin,Identification of Individuals by Means od Facial Thermography,Proceedings of The IEEE 1992 International Carnahan Conference on Security Technology: Crime Countermeasures, Atlanta,GA, USA Oct. 14-16, 1992, pp.120-125, IEEE.
[4]J. G Daugman, High Confidence Visual Recognition Neural Network Approach,IEEE Trans. Neural Network, vol.8,1997, pp.98-113.
[5]Anil K.Jain,Lin Hong,Sharath Pankanti,Biometrics:Promising frontiers for emerging identification market, MSU-CSE-00-2,February, 2000
[6]L.Hong and A.Jain,Integrating Faces and Fingerprints for Personal Identification,Proc.3th Asian Conference on Computer Vision,1998, pp.16-23,Hong Kong,China.
[7]R.Plamondon,G Lorrete, Automatic Signature Verification and Writer Identification-the State of the Art,Pattern Recognition,vol.22,1989, pp.107-131.
[8]文件检验(上册)，公安部文件检验教材，1985年1月，沈阳
[9]V. Klement, An Application System for the Computer-assisted Identification of Handwritings,Int. Carnahan Conf.On Security Technology,1983, pp.75-79.
[10]R. D. Naske, Writer Recognition by Prototype Related Deformation of Handprinted Chracters, Proc. 6th ICPR, 1982 ,pp.819-822.
[11]尺长健，金子博，淀川英司，2次统计量线分分解手书笔者认识，(日本)电子通信学会论文志，vol．J67-D，no．7，1984，pp．776-783．
[12]刘成林 戴汝为 刘迎建，基于多通道分解与匹配的笔迹鉴别研究，自动化学报，vol．23(1)，1997，pp．57-63．
[13]刘成林 戴汝为 刘迎建，简化的Wigner分布及其在笔变鉴别中的应用，计算机学报，vol．20(11)，1997，PP．1018-1023．
[14]H. E. S. Said, T. N. Tan and K D. Baker, Personal Identification Based on Handwriting, Pattern Recognition,vol.33, no.1, 2000, pp.149-160.
[15]Yong Zhu,Tieniu Tan and Yunhong Wang, Biometric Personal Identification Based on Handwriting, ICPR2000, the 15th International Conference on Pattern Recognition,2000, pp.801-804, Barcelona,Spain.
[16]吉村，吉村功，笔者认识技术最近动向，(日本)电子情报通信学会志，vol．j72-D，no．7，1989，pp．788-791．
[17]Amit Jain, Aditya Kamat,Personal Identification Based on Handwritng, the paper, 2000.
[18]W.Kuckuck,Writer Recognition by Spectral Analysis,Int. Conf.:Security through Sci. & Engng,1980,pp.5-11.
[19]B.Azari,Handwriting Identification by Means of Run-length Measurements,IEEE Trans.SMC-7, no.12, 1977, pp.878-881.
[20]W. Kuckuck, B. Rieger,and K. Steinker,Automatic Writer Recognition,Carnahan Conf.On Crime Countermeasures,1979, pp.57-64,.
[21]I.Yoshimura,M.Yoshimura,Writer Identification Based on the ARC Pattern Transformation, Proc. 9th ICPR,1988,pp.35-37.
[22]S.Impedovo,et al, An Off-line Writer Identification System Based on a Syntactic Approach,Proc.1990 IWFHR,pp.53-61.
[23]戴汝为，王钰，智能系统的互补策略，模式识别与人工智能，vol．6，no．1，1993．
[24]G.S.Peake and T. N. Tan, Script and Language Identification from Document Image, Proc. BMVC97, vol.2, 1997, pp.169-184, Essex, UK.
[25]M. Ammar, Y. Yoshida, and T. Fukumura, A New Effective Approach for Off-line Verification of Signature by Using Pressure Features, Proc.8th ICPR, 1986, pp.566-569.
[26]R.Sabourin, and R. Plamondon,Preprocessing of Handwriting Signatures from Image Gradient Analysis, Proc.8th ICPR,1986, pp.576-579.
[27]R.Sabourin, and J. P. Drouhard, Off-line Signature Verification Using Directional PDF and Neural Networks,Proc. 11th ICPR,vol.2,1992, pp.321-325.
[28]W. E. Hagan,Atreatise on Disputed Handwriting, Foundations of Ciminal Jstice,AMS,New York,1994.
[29]F.Tomita, S. Tsuji, Comuter Analysis of Visual Textures, Kluwer Academic,1990.
[30]徐建华，图像处理与分析，科学出版社，1992年．
[31]M. Tuceryan, and A. K. Jain, Texture Analysis, in C. H. Chen et al (eds),Handbook of Pattern Recognition and Computer Vision, 1993, pp.235-276,World Scientific.
[32]H.Tamura,S.Mori,T Yammawaki, Textural Features Corresponding to Visual Perception, IEEE Trans. SMC, vol. 8, no.6, 1978, pp.460-473.
[33]S.Imade,S.Tatsuta,T. Wade, Segmentation and Classification for Mixed Text/Image Documents Using Neural Network,Proc.2nd ICDAR,1993,pp.930-934.
[34]D.Wang,S.U.Srihari, Classification of Newspaper Image Blocks Using Texture Analysis, GVGIP, vol.47, 1989, pp.327-352.
[35]焦李成，保铮，子波理论与应用：进展与展望，电子学报，vol．21，no．7，1993．
[36]J. G. Daugman, Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters,J.Opt.Soc.Am.A, 2(7), 1985, pp.1160-1169.
[37]崔锦泰，小波分析导论，西安交通大学出版社，1995年．
[38] S. G Mallat, A Theory for Multiresolution Signal Decomposition: the Wavelet Representation, IEEE Trans. PAMI, 11(7) , 1989, pp.674-693．
[39] V. Klement, Forensic Writer Recognition, in J. C. Simon and R. M. Haralic (eds), Digital Image Processing, Redial Publishing Company, 1981, pp. 519-524．
[40] I. Dinstein, and Y. Shapira, Ancient Hebraic Handwriting Identification with Run-length Histograms, IEEE Trans. SMC-12, no.3,1982, pp.405-409．
[41] F. Mihelic, N. Pavesic, and L. Gyergyek, Recognition of Writer of Hand-written Texts, 1977 Int Conf. on Crime Countermeasures-Sci. & Engng, pp.237-240．
[42] B. Azari, Automatic Handwriting Identification Based on the External Properties of the Samples, IEEE Trans. System Man Cybernet, vol.13, no.l, 1983, pp.38-62．
[43] I. Yoshimura, M. Yoshimura, Adaptive Person Recognition System Based on Handwriting Characters Using the Leave-one-out Meahod, Proc. 1st Pacific Rim ICAI, 1990,pp.528-533．
[44] I. Yoshimura, M. Yoshimura, Off-line Writer Identification Using Ordinary Characters as the Object, Pattern Recognition, 1991,24(9) :909-915．
[45] M. Yoshimura, I. Yoshimura, H. B. Kim, A Text-independent Off-line Writer Identification Method for Japanese and Korea Sentences, IEICE Trans. Inf. & Syst, vol.E76-D, no.4, 1993, pp.454-461．
[46] S. Tsuruoka, et al, Handwritten Character Recognition Agaptable to the Writer, Proc. IAPR Workshop on Computer Vision, Tokyo,1988, pp.179-182．
[47] F. Kimura, et al, Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition, IEEE Trans. PAMI, 1987, 9(1) , pp. 149-153．
[48] B.-S. Jeng, et al, Chinese Character Recognition with Neural Nets Classifier, Proc. 1990 ICASSP, pp.2125-2128．
[49] Kozinets, Lantsman, Sokolov and Yakubovich, Identification and Differentiation of Handwriting with the help of Electronoic Computers, Institute of Automation and Telmechanics, Moscow 1967．
[50] A. Khotanzad, and A. Bouarfa, Image Segmentation by a Parallel, Non-parametric Histogram Based Clustering Algorithm, Pattern Recognition, vol.23, no.9,1990, pp.961-973,.
[51] I. Tehoukanot, R. Safaee-Rad, K.C. Smith, and B. Benhabib, The Angle-of-sight Signature for 2D Shape Analysis of Manufactured Objects, Pattern Recognition, vol.25, no. 11, 1992, pp. 1289-1305．
[52] E. N. Zois, and V. Anastassopoulos, Fusion of Correlated Decisions for Writer Verification, Pattern Recognition, vol.32, no.10,1999, pp.1821-1823．
[53] J. Hu, S.G Lim, and M. K. Brown, Writer Independent On-line Handwriting Recognition Using an HMM Approach, Pattern Recognition, vol.33, no.l, 2000, pp. 133-147．
[54] E. N. Zois,. and V. Anastassopoulos, Fusion of Correlated Decisions for Writer Verification, Pattern Recognition, vol.34, no.l, 2001, pp.47-61．
[55] T. N. Tan, Texture Feathure Extraction via Cortical Channel Modeling, Proc. 11th IAPR Inter. Conf. Pattern Recognition, vol.3,1992, pp.607-610．
[56] M. R. Turner, Texture Discrimination by Gabor Functions, Biol. Cybern, vol 55,
1986,pp.71-82．
[57] A.C.Bovik, M. Clark and W.B.Geisler, Multichannel Texture Analysis Using Localized Spatial Filters, IEEE Trans. Pattern Anal. Machine Intell., vol.12, no.l, 1990,pp.55-73．
[58] A.C.Bovik, N.Gopal, T. Emmoth and A. Restrepo, Localized Measurement of Emergent Image Frequencies by Gabor Wavelets, IEEE Trans. Information Theory, vol.38, no.2, 1992, pp.691-712．
[59] T. Reed, J. M. Hans, De Buf, A Review of Recent Texture Segmentation and Feature Extraction Techniques, CVGIP:Image Understanding, vol.57, 1993, pp.359-372．
[60] Anil Jain, Lin Hong, Sharath Pankanti, Biometrics: Promising frontiers for emerging identification market, Microsoft ENCARTA, 2001．
[61] A. Zramdini and R. Ingold, Optical Font Recognition Using Typographical Features, IEEE Trans. Pattern Anal. Machine Intell. vol.20, no.8, 1998, pp.877-882．
[62] Y Zhu, T. N. Tan and Y. H. Wang, Font Recognition Based on Global Texture Analysis, IEEE Trans. Pattern Anal. Machine Intell. vol.23, no. 10, 1998．
[63] http://www.ccidnet.com/news/tech/2000/10716784_9567． html [64] http://www.computerdaily.com/info/news/9906/99Q61402． html
[2]R.Clarke, Human Identification in Information Systems: Management Challenges and Public Policy Issues,Information Technology & Prople, vol.7,no.4,1994, pp.6-37.
[3]F.J. Prokoski, R. B. Riedel, and J. S.Coffin,Identification of Individuals by Means od Facial Thermography,Proceedings of The IEEE 1992 International Carnahan Conference on Security Technology: Crime Countermeasures, Atlanta,GA, USA Oct. 14-16, 1992, pp.120-125, IEEE.
[4]J. G Daugman, High Confidence Visual Recognition Neural Network Approach,IEEE Trans. Neural Network, vol.8,1997, pp.98-113.
[5]Anil K.Jain,Lin Hong,Sharath Pankanti,Biometrics:Promising frontiers for emerging identification market, MSU-CSE-00-2,February, 2000
[6]L.Hong and A.Jain,Integrating Faces and Fingerprints for Personal Identification,Proc.3th Asian Conference on Computer Vision,1998, pp.16-23,Hong Kong,China.
[7]R.Plamondon,G Lorrete, Automatic Signature Verification and Writer Identification-the State of the Art,Pattern Recognition,vol.22,1989, pp.107-131.
[8]文件检验(上册)，公安部文件检验教材，1985年1月，沈阳
[9]V. Klement, An Application System for the Computer-assisted Identification of Handwritings,Int. Carnahan Conf.On Security Technology,1983, pp.75-79.
[10]R. D. Naske, Writer Recognition by Prototype Related Deformation of Handprinted Chracters, Proc. 6th ICPR, 1982 ,pp.819-822.
[11]尺长健，金子博，淀川英司，2次统计量线分分解手书笔者认识，(日本)电子通信学会论文志，vol．J67-D，no．7，1984，pp．776-783．
[12]刘成林 戴汝为 刘迎建，基于多通道分解与匹配的笔迹鉴别研究，自动化学报，vol．23(1)，1997，pp．57-63．
[13]刘成林 戴汝为 刘迎建，简化的Wigner分布及其在笔变鉴别中的应用，计算机学报，vol．20(11)，1997，PP．1018-1023．
[14]H. E. S. Said, T. N. Tan and K D. Baker, Personal Identification Based on Handwriting, Pattern Recognition,vol.33, no.1, 2000, pp.149-160.
[15]Yong Zhu,Tieniu Tan and Yunhong Wang, Biometric Personal Identification Based on Handwriting, ICPR2000, the 15th International Conference on Pattern Recognition,2000, pp.801-804, Barcelona,Spain.
[16]吉村，吉村功，笔者认识技术最近动向，(日本)电子情报通信学会志，vol．j72-D，no．7，1989，pp．788-791．
[17]Amit Jain, Aditya Kamat,Personal Identification Based on Handwritng, the paper, 2000.
[18]W.Kuckuck,Writer Recognition by Spectral Analysis,Int. Conf.:Security through Sci. & Engng,1980,pp.5-11.
[19]B.Azari,Handwriting Identification by Means of Run-length Measurements,IEEE Trans.SMC-7, no.12, 1977, pp.878-881.
[20]W. Kuckuck, B. Rieger,and K. Steinker,Automatic Writer Recognition,Carnahan Conf.On Crime Countermeasures,1979, pp.57-64,.
[21]I.Yoshimura,M.Yoshimura,Writer Identification Based on the ARC Pattern Transformation, Proc. 9th ICPR,1988,pp.35-37.
[22]S.Impedovo,et al, An Off-line Writer Identification System Based on a Syntactic Approach,Proc.1990 IWFHR,pp.53-61.
[23]戴汝为，王钰，智能系统的互补策略，模式识别与人工智能，vol．6，no．1，1993．
[24]G.S.Peake and T. N. Tan, Script and Language Identification from Document Image, Proc. BMVC97, vol.2, 1997, pp.169-184, Essex, UK.
[25]M. Ammar, Y. Yoshida, and T. Fukumura, A New Effective Approach for Off-line Verification of Signature by Using Pressure Features, Proc.8th ICPR, 1986, pp.566-569.
[26]R.Sabourin, and R. Plamondon,Preprocessing of Handwriting Signatures from Image Gradient Analysis, Proc.8th ICPR,1986, pp.576-579.
[27]R.Sabourin, and J. P. Drouhard, Off-line Signature Verification Using Directional PDF and Neural Networks,Proc. 11th ICPR,vol.2,1992, pp.321-325.
[28]W. E. Hagan,Atreatise on Disputed Handwriting, Foundations of Ciminal Jstice,AMS,New York,1994.
[29]F.Tomita, S. Tsuji, Comuter Analysis of Visual Textures, Kluwer Academic,1990.
[30]徐建华，图像处理与分析，科学出版社，1992年．
[31]M. Tuceryan, and A. K. Jain, Texture Analysis, in C. H. Chen et al (eds),Handbook of Pattern Recognition and Computer Vision, 1993, pp.235-276,World Scientific.
[32]H.Tamura,S.Mori,T Yammawaki, Textural Features Corresponding to Visual Perception, IEEE Trans. SMC, vol. 8, no.6, 1978, pp.460-473.
[33]S.Imade,S.Tatsuta,T. Wade, Segmentation and Classification for Mixed Text/Image Documents Using Neural Network,Proc.2nd ICDAR,1993,pp.930-934.
[34]D.Wang,S.U.Srihari, Classification of Newspaper Image Blocks Using Texture Analysis, GVGIP, vol.47, 1989, pp.327-352.
[35]焦李成，保铮，子波理论与应用：进展与展望，电子学报，vol．21，no．7，1993．
[36]J. G. Daugman, Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters,J.Opt.Soc.Am.A, 2(7), 1985, pp.1160-1169.
[37]崔锦泰，小波分析导论，西安交通大学出版社，1995年．
[38] S. G Mallat, A Theory for Multiresolution Signal Decomposition: the Wavelet Representation, IEEE Trans. PAMI, 11(7) , 1989, pp.674-693．
[39] V. Klement, Forensic Writer Recognition, in J. C. Simon and R. M. Haralic (eds), Digital Image Processing, Redial Publishing Company, 1981, pp. 519-524．
[40] I. Dinstein, and Y. Shapira, Ancient Hebraic Handwriting Identification with Run-length Histograms, IEEE Trans. SMC-12, no.3,1982, pp.405-409．
[41] F. Mihelic, N. Pavesic, and L. Gyergyek, Recognition of Writer of Hand-written Texts, 1977 Int Conf. on Crime Countermeasures-Sci. & Engng, pp.237-240．
[42] B. Azari, Automatic Handwriting Identification Based on the External Properties of the Samples, IEEE Trans. System Man Cybernet, vol.13, no.l, 1983, pp.38-62．
[43] I. Yoshimura, M. Yoshimura, Adaptive Person Recognition System Based on Handwriting Characters Using the Leave-one-out Meahod, Proc. 1st Pacific Rim ICAI, 1990,pp.528-533．
[44] I. Yoshimura, M. Yoshimura, Off-line Writer Identification Using Ordinary Characters as the Object, Pattern Recognition, 1991,24(9) :909-915．
[45] M. Yoshimura, I. Yoshimura, H. B. Kim, A Text-independent Off-line Writer Identification Method for Japanese and Korea Sentences, IEICE Trans. Inf. & Syst, vol.E76-D, no.4, 1993, pp.454-461．
[46] S. Tsuruoka, et al, Handwritten Character Recognition Agaptable to the Writer, Proc. IAPR Workshop on Computer Vision, Tokyo,1988, pp.179-182．
[47] F. Kimura, et al, Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition, IEEE Trans. PAMI, 1987, 9(1) , pp. 149-153．
[48] B.-S. Jeng, et al, Chinese Character Recognition with Neural Nets Classifier, Proc. 1990 ICASSP, pp.2125-2128．
[49] Kozinets, Lantsman, Sokolov and Yakubovich, Identification and Differentiation of Handwriting with the help of Electronoic Computers, Institute of Automation and Telmechanics, Moscow 1967．
[50] A. Khotanzad, and A. Bouarfa, Image Segmentation by a Parallel, Non-parametric Histogram Based Clustering Algorithm, Pattern Recognition, vol.23, no.9,1990, pp.961-973,.
[51] I. Tehoukanot, R. Safaee-Rad, K.C. Smith, and B. Benhabib, The Angle-of-sight Signature for 2D Shape Analysis of Manufactured Objects, Pattern Recognition, vol.25, no. 11, 1992, pp. 1289-1305．
[52] E. N. Zois, and V. Anastassopoulos, Fusion of Correlated Decisions for Writer Verification, Pattern Recognition, vol.32, no.10,1999, pp.1821-1823．
[53] J. Hu, S.G Lim, and M. K. Brown, Writer Independent On-line Handwriting Recognition Using an HMM Approach, Pattern Recognition, vol.33, no.l, 2000, pp. 133-147．
[54] E. N. Zois,. and V. Anastassopoulos, Fusion of Correlated Decisions for Writer Verification, Pattern Recognition, vol.34, no.l, 2001, pp.47-61．
[55] T. N. Tan, Texture Feathure Extraction via Cortical Channel Modeling, Proc. 11th IAPR Inter. Conf. Pattern Recognition, vol.3,1992, pp.607-610．
[56] M. R. Turner, Texture Discrimination by Gabor Functions, Biol. Cybern, vol 55,
1986,pp.71-82．
[57] A.C.Bovik, M. Clark and W.B.Geisler, Multichannel Texture Analysis Using Localized Spatial Filters, IEEE Trans. Pattern Anal. Machine Intell., vol.12, no.l, 1990,pp.55-73．
[58] A.C.Bovik, N.Gopal, T. Emmoth and A. Restrepo, Localized Measurement of Emergent Image Frequencies by Gabor Wavelets, IEEE Trans. Information Theory, vol.38, no.2, 1992, pp.691-712．
[59] T. Reed, J. M. Hans, De Buf, A Review of Recent Texture Segmentation and Feature Extraction Techniques, CVGIP:Image Understanding, vol.57, 1993, pp.359-372．
[60] Anil Jain, Lin Hong, Sharath Pankanti, Biometrics: Promising frontiers for emerging identification market, Microsoft ENCARTA, 2001．
[61] A. Zramdini and R. Ingold, Optical Font Recognition Using Typographical Features, IEEE Trans. Pattern Anal. Machine Intell. vol.20, no.8, 1998, pp.877-882．
[62] Y Zhu, T. N. Tan and Y. H. Wang, Font Recognition Based on Global Texture Analysis, IEEE Trans. Pattern Anal. Machine Intell. vol.23, no. 10, 1998．
[63] http://www.ccidnet.com/news/tech/2000/10716784_9567． html [64] http://www.computerdaily.com/info/news/9906/99Q61402． html