Abstract
This paper presents a study of 25 structural features extracted from samples of grapheme ‘th’ that correspond to features commonly used by forensic document examiners. Most of the features are extracted using vector skeletons produced by a specially developed skeletonisation algorithm. The methods of feature extraction are presented along with the results. Analysis of the usefulness of the features was conducted and three categories of features were identified: indispensable, partially relevant and irrelevant for determining the authorship of genuine unconstrained handwriting. The division was performed based on searching the optimal feature sets using the wrapper method. A constructive neural network was used as a classifier and a genetic algorithm was used to search for optimal feature sets. It is shown that structural micro features similar to those used in forensic document analysis do possess discriminative power. The results are also compared to those obtained in our preceding study, and it is shown that use of the vector skeletonisation allows both extraction of more structural features and improvement the feature extraction accuracy from 87% to 94%.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hilton, O.: Scientific Examination of Questioned Documents. CRC Hall, Florida (1993)
Robertson, E.W.: Fundamentals of Document Examination. Nelson-Hall, Illinois (1991)
Harrison, W.R.: Suspect Documents, Their Scientific Examinations. Nelson-Hall, Illinois (1981)
Lindblom, B.: Document examination. In: Chayko, G.M., Gulliver, E.D. (eds.) Forensic Evidence in Canada, 2nd edn., pp. 505–525. Canada Law Book, Aurora (1999)
Daubert et al. v. Merrell Dow Pharmaceuticals. Court case 509 U. S. 579 (1993)
Kam, M., Fielding, G., Conn, R.: Writer identification by professional document examiners. Journal of Forensic Sciences 42, 778–786 (1997)
Found, B., Sita, J., Rogers, D.: The development of a program for characterizing forensic handwriting examiners’ expertise: Signature examination pilot study. Journal of Forensic Document Examination 12, 69–80 (1999)
Kam, M., Gummadidala, K., Fielding, G., Conn, R.: Signature authentication by forensic document examiners. Journal of Forensic Science 46, 884–888 (2001)
Sita, J., Found, B., Rogers, D.: Forensic handwriting examiners’ expertise for signature comparison. Journal of Forensic Sciences 47, 1117–1124 (2001)
Srihari, S.N., Cha, S.H., Arora, H., Lee, S.: Individuality of handwriting. Journal of Forensic Sciences 47, 1–17 (2002)
Found, B., Roger, D., Schmittat, R.: A computer program designed to compare the spatial elements of handwriting. Forensic Science International 68, 195–203 (1994)
Schomaker, L., Bulacu, M., van Erp, M.: Sparse-parametric writer identification using heterogeneous feature groups. In: Proc. 3rd Int’l Conf. Document Analysis and Recognition (ICDAR 1995), Montreal, Canada, pp. 545–548 (1995)
Srihari, S.N., Cha, S.H., Lee, S.: Establishing handwriting individuality using pattern recognition techniques. In: Proc. 6th Int’l Conf. Document Analysis and Recognition (ICDAR 2001), Seattle, USA, pp. 1195–1204 (2001)
Tomai, C.I., Zhang, B., Srihari, S.N.: Discriminatory power of handwritten words for writer recognition. In: Proc. 17th Int’l Conf. Pattern Recognition, Cambridge, UK, pp. 638–641 (2004)
Zhang, B., Srihari, S.N., Lee, S.: Individuality of handwritten characters. In: Proc. 7th Int’l Conf. Document Analysis and Recognition (ICDAR 2003), Edinburgh, UK, pp. 1086–1090 (2003)
Srihari, S.N., Tomai, C.I., Zhang, B., Lee, S.: Individuality of numerals. In: Proc. 7th Int’l Conf. Document Analysis and Recognition (ICDAR 2003), Edinburgh, UK, pp. 1096–1100 (2003)
Huber, R.A., Headrick, A.M.: Handwriting Identification: Facts and Fundamentals. CRC Press, LCC (1999)
Leedham, C.G., Pervouchine, V., Tan, W.K., Jacob, A.: Automatic quantitative letter-level extraction of features used by document examiners. In: Teulings, H.L., Van Gemmert, A.W.A. (eds.) Proc. 11th Conf. Int’l. Graphonomics Society (IGS 2003), Scottsdale, AZ, USA, pp. 291–294 (2003)
Leedham, C.G., Pervouchine, V., Tan, W.K.: Quantitative letter-level extraction and analysis of features used by document examiners. Journal of Forensic Document Examination (2004) (in press)
Leedham, C.G., Pervouchine, V.: Validating the use of handwriting as a biometric and its forensic analysis. In: Pal, U., Parui, S.K., Chaudhuri, B.B. (eds.) Document Analysis: Proc. Int’l. Workshop on Document Analysis (IWDA 2005), pp. 175–192. Allied Publishers Ltd, Chennai (2005) (Invited lecture)
Pervouchine, V., Leedham, C.G., Melikhov, K.: Handwritten character skeletonisation for forensic document analysis. In: Proc. 20th Annual ACM Symposium on Applied Computing, Santa Fe, NM, USA, pp. 754–758 (2005)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proc. 11th Int’l Conf. Machine Learning (ML 1994), Rutgers University, New Brunswick, NJ, pp. 121–129 (1994)
Weiss, S.M., Kulikowski, C.A.: Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets. In: Machine Learning, and Expert Systems. Morgan Kaufmann, San Francisco (1991)
Yang, J., Parekh, R., Honavar, V.: Distal: An inter-pattern distance-based constructive learning algorithm. Technical Report ISU-CS-TR 97-06, Department of Computer Science, Iowa State University (1997); Also appeared in Proc. Int’l. Conf. Neural Networks. IEEE, Piscataway (1998)
Vafaie, H., De Jong, K.: Genetic algorithms as a tool for feature selection in machine learning. In: Proc. 4th Int’l Conf. Tools with Artificial Intelligence (TAI 1992), pp. 200–203. IEEE Computer Society Press, Arlington (1992)
Brill, F.Z., Brown, D.E., Martin, W.N.: Fast genetic selection of features for neural network classifiers. IEEE Trans. Neural Networks 3, 324–328 (1992)
Bala, J., Huang, J., Vafaie, H., De Jong, K., Wechsler, H.: Hybrid learning using genetic algorithms and decision trees for pattern classification. In: Proc. Int’l Joint Conf. Artificial Intelligence (IJCAI 1995), Montreal, Canada (1995)
Chen, S., Smith, S., Guerra-Salcedo, C., Whitley, D.: Fast and accurate feature selection using hybrid genetic strategies. In: Proc. Congress on Evolutionary Computation (CEC 1999), Washington DC, USA (1999)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intell. Syst. 13, 44–49 (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Syswerda, G.: Uniform crossover in genetic algorithms. In: Proc. 3rd Int’l Conf. Genetic Algorithms, George Mason University, USA, pp. 2–9. Morgan Kaufmann, San Francisco (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pervouchine, V., Leedham, G. (2006). Extraction and Analysis of Document Examiner Features from Vector Skeletons of Grapheme ‘th’. In: Bunke, H., Spitz, A.L. (eds) Document Analysis Systems VII. DAS 2006. Lecture Notes in Computer Science, vol 3872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11669487_18
Download citation
DOI: https://doi.org/10.1007/11669487_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32140-8
Online ISBN: 978-3-540-32157-6
eBook Packages: Computer ScienceComputer Science (R0)