Skip to main content
SpringerLink
Log in

A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis

  • Chapter
Book cover Computational Intelligence in Digital Forensics: Forensic Investigation and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 555))

  • 1636 Accesses

Abstract

Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual’s handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual’s writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amend, K., Ruiz, M.S.: Handwriting Analysis. The Career Press, New Jersey (1980)

    Google Scholar 

  2. Huber, R.A., Headrick, A.M.: Handwriting Identification: Facts and Fundamentals. CRC Press, New York (1999)

    Book  Google Scholar 

  3. Baranoski, F.L., Oliveira, L.S., Justino, E.J.R.: Writer Identification Based on Forensic Science Approach. In: XXXIII Latin American Conference on Informatics, San Jose, Costa Rica, pp. 25–32 (2007)

    Google Scholar 

  4. Muda, A.K.: Authorship Invarianceness for Writer Identification Using Invariant Discretization and Modified Immune Classifier. Universiti Teknologi Malaysia (2009)

    Google Scholar 

  5. Li, J., Zheng, R., Chen, H.: From Fingerprint to Writeprint. Communications of the ACM 49(4), 76–82 (2006)

    Article  Google Scholar 

  6. Al-Ma’adeed, S., Mohammed, E., Kassis, D.A., Al-Muslih, F.: Writer Identification Using Edge-based Directional Probability Distribution Features for Arabic Words. In: Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications 2008, pp. 582–590. IEEE Computer Society (2008), 1544403

    Google Scholar 

  7. Niels, R., Vuurpijl, L., Schomaker, L.: Automatic Allograph Matching in Forensic Writer Identification. International Journal of Pattern Recognition and Artificial Intelligence 21(1), 61–81 (2007), doi:10.1142/S0218001407005302

    Article  Google Scholar 

  8. Pervouchine, V., Leedham, G.: Extraction and Analysis of Forensic Document Examiner Features Used for Writer Identification. Pattern Recognition 40(3), 1004–1013 (2007), doi:10.1016/j.patcog.2006.08.008

    Article  MATH  Google Scholar 

  9. Srihari, S.N., Huang, C., Srinivasan, H., Shah, V.: Biometric and Forensic Aspects of Digital Document Processing. In: Chaudhuri, B.B. (ed.) Digital Document Processing. Advances in Pattern Recognition, pp. 379–405. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Tapiador, M., Sigüenza, J.: Writer Identification Method Based on Forensic Knowledge. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 555–561. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Srihari, S.N., Cha, S.-H., Arora, H., Lee, S.: Individuality of Handwriting. Journal of Forensic Sciences, 856–872 (2002)

    Google Scholar 

  12. Franke, K., Köppen, M.: A Computer-based System to Support Forensic Studies on Handwritten Documents. International Journal on Document Analysis and Recognition 3(4), 218–231 (2001), doi:10.1007/PL00013565

    Article  Google Scholar 

  13. Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten Digit Recognition: Benchmarking of the State-of-the-art Techniques. Pattern Recognition 36(10), 2271–2285 (2003)

    Article  MATH  Google Scholar 

  14. Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten Digit Recognition: Investigation of Normalization and Feature Extraction Techniques. Pattern Recognition 37(2), 265–279 (2004)

    Article  MATH  Google Scholar 

  15. Xu, D.-Y., Shang, Z.-W., Tang, Y.-Y., Fang, B.: Handwriting-based Writer Identification with Complex Wavelet Transform. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 597–601 (2008)

    Google Scholar 

  16. Bensefia, A., Paquet, T., Heutte, L.: A Writer Identification and Verification System. Pattern Recognition Letters 26(13), 2080–2092 (2005)

    Article  Google Scholar 

  17. He, Z.-Y., Tang, Y.-Y.: Chinese Handwriting-based Writer Identification by Texture Analysis. In: Proceedings of International Conference on Machine Learning and Cybernetics, pp. 3488–3491 (2004)

    Google Scholar 

  18. Yu, K., Wang, Y., Tan, T.: Writer Identification Using Dynamic Features. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 512–518. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. Schlapbach, A., Bunke, H.: Off-line Handwriting Identification Using HMM Based Recognizers. In: 17th International Conference on Pattern Recognition, Cambridge, pp. 654–658 (2004)

    Google Scholar 

  20. Shen, C., Ruan, X.-G., Mao, T.-L.: Writer Identification Using Gabor Wavelet. In: Proceedings of the 4th World Congress on Intelligent Control and Automation, pp. 2061–2064 (2002)

    Google Scholar 

  21. Gupta, S.: Automatic Person Identification and Verification using Online Handwriting. International Institute of Information Technology (2008)

    Google Scholar 

  22. Bulacu, M.L.: Statistical Pattern Recognition for Automatic Writer Identification and Verification. University of Groningen (2007)

    Google Scholar 

  23. Zhang, B., Srihari, S.N.: Analysis of Handwriting Individuality Using Word Features. In: Proceedings of the Seventh International Conference of Document Analysis and Recognition, pp. 1142–1146 (2003)

    Google Scholar 

  24. Marti, U.V., Messerli, R., Bunke, H.: Writer Identification Using Text Line Based Features. In: Proceedings of Sixth International Conference on Document Analysis and Recognition, pp. 101–105 (2001)

    Google Scholar 

  25. Srihari, S.N., Cha, S.-H., Arora, H., Lee, S.: Individuality of Handwriting: A Validation Study. In: Sixth IAPR International Conference on Document Analysis and Recognition, pp. 106–109 (2001)

    Google Scholar 

  26. Yong, Z., Tieniu, T., Yunhong, W.: Biometric Personal Identification Based on Handwriting. In: Proceedings of 15th International Conference on Pattern Recognition, pp. 797–800 (2000)

    Google Scholar 

  27. Zheng, Z., Srihari, R., Srihari, S.N.: A Feature Selection Framework for Text Filtering. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), pp. 705–708. IEEE (2003)

    Google Scholar 

  28. Srihari, S.H., Cha, S.-H., Lee, S.: Establishing Handwriting Individuality Using Pattern Recognition Techniques. In: Sixth International Conference on Document Analysis and Recognition, pp. 1195–1204 (2001)

    Google Scholar 

  29. Schlapbach, A., Kilchherr, V., Bunke, H.: Improving Writer Identification by Means of Feature Selection and Extraction. In: Eight International Conference on Document Analysis and Recognition, pp. 131–135. IEEE (2005)

    Google Scholar 

  30. Said, H.E.S., Tan, T., Baker, K.: Personal Identification Based on Handwriting. Pattern Recognition 33, 149–160 (2000)

    Article  Google Scholar 

  31. Cha, S.-H., Srihari, S.: Multiple Feature Integration for Writer Verification. In: Proceedings of 7th International Workshop on Frontiers in Handwriting Recognition, pp. 333–342 (2000)

    Google Scholar 

  32. Zhang, B., Srihari, S.N., Lee, S.: Individuality of Handwritten Characters. In: Proceedings of 7th International Conference on Document Analysis and Recognition, pp. 1086–1090 (2003)

    Google Scholar 

  33. Zois, E.N., Anastassopoulos, V.: Morphological Waveform Coding for Writer Identification. Pattern Recognition 33, 385–398 (2000)

    Article  Google Scholar 

  34. Bulacu, M., Schomaker, L., Vuurpijl, L.: Writer Identification Using Edge-based Directional Features. In: Proceedings of 7th International Conference on Document Analysis and Recognition, pp. 937–941 (2003)

    Google Scholar 

  35. Schomaker, L., Bulacu, M.: Automatic Writer Identification using Connected-component Contours and Edge-based Features of Uppercase Western Script. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 787–798 (2004)

    Article  Google Scholar 

  36. Bensefia, A., Paquet, T., Heutte, L.: Handwriting Analysis for Writer Verification. In: Proceedings of 9th International Workshop on Frontiers in Handwriting Recognition, pp. 196–201 (2004)

    Google Scholar 

  37. Leedham, G., Chachra, S.: Writer Identification Using Innovative Binarised Features of Handwritten Numerals. In: Proceedings of 7th International Conference on Document Analysis and Recognition, pp. 413–417 (2003)

    Google Scholar 

  38. Siddiqi, I.: Classification of Handwritten Documents: Writer Recognition. Université Paris Descartes (2009)

    Google Scholar 

  39. He, Z., Youb, X., Tang, Y.-Y.: Writer Identification Using Global Wavelet-based Features. Neurocomputing 71(10), 1831–1841 (2008)

    Google Scholar 

  40. Yu, L., Liu, H.: Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research, 1205–1224 (2004)

    Google Scholar 

  41. Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. University of Waikato (1999)

    Google Scholar 

  42. Dash, M., Liu, H.: Feature Selection for Classification. Journal of Intelligent Data Analysis, 131–156 (1997)

    Google Scholar 

  43. Zhang, P., Bui, T.D., Suen, C.Y.: Feature Dimensionality Reduction for the Verification of Handwritten Numerals. Pattern Analysis Application, 296–307 (2004)

    Google Scholar 

  44. Kim, G., Kim, S.: Feature Selection Using Genetic Algorithms for Handwritten Character Recognition. In: Seventh International Workshop on Frontiers in Handwriting Recognition, Amsterdam, pp. 103–112. International Unipen Foundation (2000)

    Google Scholar 

  45. Sewell, M.: Feature Selection (2007), http://machine-learning.martinsewell.com/feature-selection/feature-selection.pdf (accessed October 25, 2009)

  46. Ahmad, A., Dey, L.: A Feature Selection Technique for Classificatory Analysis. Pattern Recognition Letters, 43–56 (2004)

    Google Scholar 

  47. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)

    MATH  Google Scholar 

  48. Portinale, L., Saitta, L.: Feature Selection: State of the Art. In: Feature Selection, pp. 1–22. Universita del Piemonte Orientale, Alessandria (2002)

    Google Scholar 

  49. Kudo, M., Sklansky, J.: Comparison of Algorithms that Select Features for Pattern Classifiers. Journal of Pattern Recognition 33, 25–41 (2000)

    Article  Google Scholar 

  50. Yinan, S., Weijun, L., Yuechao, W.: United Moment Invariants for Shape Discrimination. In: International Conference on Robotics, Intelligent Systems and Signal Processing, Changsha, pp. 88–93. IEEE (2003)

    Google Scholar 

  51. Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory, 179–187 (1962)

    Google Scholar 

  52. Flusser, J., Suk, T., Zitová, B.: Moments and Moment Invariants in Pattern Recognition, vol. 1. John Wiley and Sons, Ltd., West Sussex (2009)

    Book  MATH  Google Scholar 

  53. Belkasim, S.O., Shridhar, M., Ahmadi, M.: Pattern Recognition with Moment Invariants: A Comparative Study and New Results. Pattern Recognition 24(12), 1117–1138 (1991)

    Article  Google Scholar 

  54. Ding, M., Chang, J., Peng, J.: Research on Moment Invariants Algorithm. Journal of Data Acquisition and Processing 7(2), 1–9 (1992)

    Google Scholar 

  55. Lv, H., Zhou, J.: Research on Discrete Moment Invariance Algorithm. Journal of Data Acquisition and Processing 1(2), 151–155 (1993)

    Google Scholar 

  56. Wang, B., Sun, J., Cai, A.: Relative Moments and Their Applications to Geometric Shape Recognition. Journal of Image and Graphics 6(3), 296–300 (2002)

    Google Scholar 

  57. Liu, J., Zhang, T.: Construction and Expansion of Target’s Moment Invariants. The Transaction of Photo Electricity Technique 2002, 123–130 (2002)

    Google Scholar 

  58. Mukundan, R., Ramakrishnan, K.R.: Moment Functions in Image Analysis Theory and Application. World Scientific Publishing Co. Pte. Ltd., Singapore (1998)

    Book  Google Scholar 

  59. Vinciarelli, A.: A Survey on Off-line Cursive Word Recognition. Pattern Recognition, 1433–1446 (2002)

    Google Scholar 

  60. Madvanath, S., Govindaraju, V.: The Role of Holistic Paradigms in Handwritten Word Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 149–164 (2001)

    Google Scholar 

  61. Agre, G., Peev, S.: On Supervised and Unsupervised Discretization. Cybernetics and Information Technologies 2(2), 43–57 (2002)

    Google Scholar 

  62. Nguyen, H.S.: Discretization Problems for Rough Set Methods. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 545–552. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  63. Xin, G., Xiao, Y., You, H.: Discretization of Continuous Interval-valued Attributes in Rough Set Theory and Application. In: International Conference on Machine Learning and Cybernetics, pp. 3682–3686 (2007)

    Google Scholar 

  64. Liu, H., Hussain, F., Tan, C.-L., Dash, M.: Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6, 292–423 (2002)

    MathSciNet  Google Scholar 

  65. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and Unsupervised Discretization of Continuous Features. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 194–202 (1995)

    Google Scholar 

  66. Kotsiantis, S., Kanellopoulos, D.: Discretization Techniques: A recent survey. GESTS International Transactions on Computer Science and Engineering 32(1), 47–58 (2006)

    Google Scholar 

  67. Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Artificial Intelligence 97(1-2), 1–43 (1997)

    Article  Google Scholar 

  68. Gadat, S., Younes, L.: A Stochastic Algorithm for Feature Selection in Pattern Recognition. Journal of Machine Learning Research, 509–547 (2007)

    Google Scholar 

  69. Saeys, Y., Inza, I., Larranaga, P.: A Review of Feature Selection Techniques in Bioinformatics. Journal of Bioinformatics, 2507–2517 (2007)

    Google Scholar 

  70. Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., Wang, S.: An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering 8(2), 191–200 (2011)

    Article  Google Scholar 

  71. Unler, A., Murat, A.: A Discrete Particle Swarm Optimization Method for Feature Selection in Binary Classification Problems. European Journal of Operational Research 206, 528–539 (2010)

    Article  MATH  Google Scholar 

  72. Deriche, M.: Feature Selection Using Ant Colony Optimization. In: 6th International Multi-Conference on Systems, Signals and Devices, pp. 1–4 (2009)

    Google Scholar 

  73. Zhuo, L., Zheng, J., Wang, F., Li, X., Ai, B., Qian, J.: A Genetic Algorithm Based Wrapper Feature Selection Method for Classification of Hyperspectral Images Using Support Vector Machine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37, 397–402 (2008)

    Google Scholar 

  74. Subbotin, S., Oleynik, A.: Modifications of Ant Colony Optimization Method for Feature Selection. In: 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics, pp. 493–494 (2007)

    Google Scholar 

  75. Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters 28(4), 459–471 (2007)

    Article  Google Scholar 

  76. Ververidis, D., Kotropoulos, C.: Fast and Accurate Sequential Floating Forward Feature Selection with the Bayes Classifier Applied to Speech Emotion Recognition. Signal Processing 88(12), 2956–2970 (2008)

    Article  MATH  Google Scholar 

  77. Xie, J., Xie, W., Wang, C., Gao, X.: A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases. In: Workshop on Applications of Pattern Analysis, pp. 142–151 (2010)

    Google Scholar 

  78. Chuang, L.-Y., Chang, H.-W., Tu, C.-J., Yang, C.-H.: Improved Binary PSO for Feature Selection using Gene Expression Data. Computational Biology and Chemistry 32(1), 29–38 (2008)

    Article  MATH  Google Scholar 

  79. Deisy, C., Subbulakshmi, B., Baskar, S., Ramaraj, N.: Efficient Dimensionality Reduction Approaches for Feature Selection. In: International Conference on Computational Intelligence and Multimedia Applications, pp. 121–127 (2007)

    Google Scholar 

  80. Pizzi, N.J., Pedtycz, W.: Classification of Magnetic Resonance Spectra using Parallel Randomized Feature Selection. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, pp. 2455–2459 (2004)

    Google Scholar 

  81. Melab, N., Cahon, S., Talbi, E.-G.: Parallel GA-based Wrapper Feature Selection for Spectroscopic Data Mining. In: Proceedings of International Parallel and Distributed Processing Symposium, pp. 201–208 (2002)

    Google Scholar 

  82. Qian, X.-J., Xu, J.-B.: Optimization and Implementation of Sorting Algorithm Based on Multi-core and Multi-thread. In: IEEE 3rd International Conference on Communication Software and Networks, pp. 29–32 (2011)

    Google Scholar 

  83. Cruz, C., Pelta, D.A., Royo, A.S., Verdegay, J.L.: Soft Computing and Cooperative Strategies for Optimization. In: IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications 2005, pp. 75–78 (2005)

    Google Scholar 

  84. Pudil, P., Novovicova, J., Kittler, J.: Floating Search Methods in Feature Selection. Pattern Recognition Letters 15, 1119–1125 (1994)

    Article  Google Scholar 

  85. Whitney, A.W.: A Direct Method of Nonparametric Measurement Selection. IEEE Transaction in Computational, 1100–1103 (1971)

    Google Scholar 

  86. Eberhart, R.C., Kennedy, J.: A New Optimizer using Particle Swarm Theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  87. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks 1995, pp. 1942–1948 (1995)

    Google Scholar 

  88. Abdl, K.M., Mohd Hashim, S.Z.: Swarm-Based Feature Selection for Handwriting Identification. Journal of Computer Science 6(1), 80–86 (2010)

    Article  Google Scholar 

  89. Pratama, S.F., Muda, A.K., Choo, Y.-H., Muda, N.A.: A Comparative Study of Feature Selection Methods for Authorship Invarianceness in Writer Identification. International Journal of Computer Information Systems and Industrial Management Applications 4, 467–476 (2012)

    Google Scholar 

  90. Pratama, S.F., Muda, A.K., Choo, Y.-H., Muda, N.A.: PSO and Computationally Inexpensive Sequential Forward Floating Selection in Acquiring Significant Features for Handwritten Authorship. In: 11th International Conference on Hybrid Intelligent Systems, Melaka, Malaysia, pp. 358–363 (2011)

    Google Scholar 

  91. Pratama, S.F., Muda, A.K., Choo, Y.-H., Muda, N.A.: Computationally Inexpensive Sequential Forward Floating Selection for Acquiring Significant Features for Authorship Invarianceness in Writer Identification. International Journal of New Computer Architectures and Their Applications 1(3), 581–598 (2011)

    Google Scholar 

  92. Pratama, S.F., Muda, A.K., Choo, Y.-H., Muda, N.A.: SOCIFS Feature Selection Framework for Handwritten Authorship. International Journal of Hybrid Intelligent Systems 10(2), 83–91 (2013), doi:10.3233/HIS-130167

    Google Scholar 

  93. Muda, A.K., Shamsuddin, S.M., Darus, M.: Invariants Discretization for Individuality Representation in Handwritten Authorship. In: Srihari, S.N., Franke, K. (eds.) IWCF 2008. LNCS, vol. 5158, pp. 218–228. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  94. Pineda-Bautista, B.B., Carrasco-Ochoa, J.A., Martinez-Trinidad, J.F.: General Framework for Class-Specific Feature Selection. Expert Systems with Applications 38, 10018–10024 (2011)

    Article  Google Scholar 

  95. Marti, U., Bunke, H.: The IAM-database: an English Sentence Database for Off-line Handwriting Recognition. International Journal on Document Analysis and Recognition 5, 39–46 (2002)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence and Technologies (CIT) Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Satrya Fajri Pratama, Azah Kamilah Muda, Yun-Huoy Choo & Noor Azilah Muda

Authors
  1. Satrya Fajri Pratama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Azah Kamilah Muda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-Huoy Choo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noor Azilah Muda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satrya Fajri Pratama .

Editor information

Editors and Affiliations

  1. Department of Software Engineering Faculty of Information and, Technical University of Malaysia Melaka (UTeM), Durian Tunggal, Malaysia

    Azah Kamilah Muda

  2. Department of Software Engineering Faculty of Info. and Comm. Tech., Technical University of Malaysia Melaka (UTeM), Durian Tunggal, Malaysia

    Yun-Huoy Choo

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, Washington, USA

    Ajith Abraham

  4. Dept. of Computer Sci. and Engineering Center of Excellence for Document, The State University of New York SUNY, Buffalo, New York, USA

    Sargur N. Srihari

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Pratama, S.F., Muda, A.K., Choo, YH., Muda, N.A. (2014). A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis. In: Muda, A., Choo, YH., Abraham, A., N. Srihari, S. (eds) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications. Studies in Computational Intelligence, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-319-05885-6_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05885-6_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05884-9

  • Online ISBN: 978-3-319-05885-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation