Determination of Evidence Evaluation of Individual Characteristics Used in Handwriting Analysis According to the Frequencies in Database

  • Dilara Öner
  • Salih Cengiz
  • Gürsel Çetin
Keywords: Document Examination, Handwriting Comparisons, Database, Likelihood Ratio, Evidence Evaluation


Objective: The aim of the study is to determine the reliability of the results gained from the hand-writing investigations and comparisons mathematically by using likelihood ratios in order to form a database.

Materials and Methods: For this purpose, 500 people graduated from university or post-graduate institutions or still studying, were used to write the letters and numbers one by one and a special text containing all the letters and numbers with punctuation and connections, twice. All the written samples were scanned with high resolution and transferred to a computer.

A sentence which has a criminal offense were written in an effort to escape without changing some of the letters by different individuals. Three experts who are not authored in this publication were offered to compare the sentences with the collected samples and to determine the characteristics that will show belonging.

Results: The similarities for the construction and forms of the letters that were determined by the experts were searched in database, and the frequencies of these similarities in databases and society were determined to elucidate the mathematical reliability of the results obtained from the study.

Conclusion: In recent years, there are lots of scientific studies related with this topic and this study was performed under the light of these studies. Some characteristics determined as similarity were highly seen in the Database whereas some characteristics were rarely seen. As a result, it was concluded for an expert it has a great importance that he or she should use either a general database or create a database from the archived samples.


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Aitken CGG, Taroni F. Statistics and the evalution of evidence for forensic scientists. 2. baskı , Chichester: John Wiley& Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, 2004.

Adam C,.Essential mathematics and statistics for forensic science. 1.baskı, Chichester: John Wiley & Sons, Ltd, The Atrium , Southern Gate, Chichester, West Sussex PO198SQ, England, 2010.

Lucy D. Introduction to Statistics for Forensic Scientists. 1. baskı, Chichester: John Wiley & Sons, Ltd, The Atrium , Southern Gate, Chichester, West Sussex PO198SQ, England, 2005.

Taroni F, Marquis R, Schmittbuhl M, Biedermann A, Thie´ry A, Bozza S. The use of the likelihood ratio for evaluative and investigative purposes in comparative forensic handwriting examination, Forensic Science International, 2012;214:189-194

Tang Y, Srihari SN. Likelihood ratio estimation in forensic identification using similarity and rarity, Pattern Recognition, 2014;47:945-958

Morrison GF. Measuring the validity and reliability of forensic likelihood-ratio systems, Science and Justice, 2011;51: 91-98

Srihari SN, Singer K. Role of automation in the examination of hand written items, Pattern Recognition, 2014;47:1083-1095

Biedermann A, Voisard R, Taroni F. Learning about Bayesian networks awardfor forensic interpretation: An example based on the problem of multiple propositions, Science and Justice, 2012;52:191-198

Forster MR. Counterexamples to a likelihood theory of evidence, Journal Minds and Machines, 2006;16:319-338

Srihari SN, Srinivasan H. Comparison of ROC and likelihood decision methods in automatic fingerprint verification, International Journal of Pattern Recognition and Artificial Intelligence, 2008;22:535-553

Srihari SN, Huang C, Srinivasan, H. On the discriminability of the handwriting of twins, Journal of Forensic Science, 2008;53:430-446

Evett IW, Jackson G, Lambert JA, McCrossan S. The impact of the principles of evidence interpretation on the structure and content of statements, Science & Justice, 2000;40:233-239

Xu Z, Srihari SN. Bayesian network structure learning and Inference Methods for Handwriting, Proceeding of 12th International Conference on Document Analysis and Recognition;2013 1320 – 1324: Washington, DC doi:10.1109/ICDAR.2013.267

Marquis R, Taroni F, Bozza S, Schmittbuhl M. Quantitative characterization of morphological polymorphism of handwritten characters loops, Forensic Science International, 2006;164:211-220

Bozza S, Taroni F, Marquis R, Schmittbuhl M. Probabilistic evaluation of handwriting evidence: likelihood ratio for authorship, Journal of the Royal Statistical Society, 2008;57:329-341

Cardinetti B, Cammarota C, Negative conclusion cases: further proposal for likelihood ratio evaluation, Law, Probability and Risk Advance, 2007:1-16, doi:10.1093/lpr/mgl018

Srihari SN. Determining writership of historical manuscripts using computational methods, Automatic Pattern Recognition and Historical Handwriting Analysis workshop presented at Erlengen, Germany, June 14-15, 2013

Srihari SN, Tomai CI, Zang B, Sangjik L. Individuality of Numerals. Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2013)

Srihari S, Huang C, Srinivasan H. Content-based information retrieval from handwritten documents, U.S. Department of Justice, National Institute of Justice grant 2002-LT-BX-K007

Nordgaard A, Ansell R, Drotz W, Jaeger L. Scale of conclusions fort he value of evidence, Law Probability and Risk, 2011;1-24 doi:10.1093/lpr/mgr020

Aitken CGG, Taroni F. A verbal scale for the interpretation of evidence, Science & Justice, 38;(1998): 279-283

Cereda G. Impact of model choice on LR assessment in case of rare haplotype match (frequentist approach), 2015 arXiv:1502.04083

Taroni F, Aitken C, Garbolino P, Biedermann A, Bayesian Networks and Probabilistic Inference in Forensic Science. Barnett V, editör, 2006, doi: 10.1002/0470091754

Biedermann A, Taroni F. Findings evaluation in forensic manuscript examination: Necessity of a logical approach. Kriminalistik, 2005; 59:369-370

Taroni F, Bozza S, Biedermann A, Aitken C. Dismissal of the illusion of uncertainty in the assessment of a likelihood ratio, Law Probability and Risk, 2016;15:1-16 doi: 10.1093/lpr/mgv008

Godambe VP, Finetti Bd. Probability, Induction and Statistics. Journal of the American Statistical Association, 1974: 69(3+010120001+0):578 doi: 10.2307/2285706

Dørum G, Bleka Ø, Gill P, Egeland T. Exact computation of the distribution of likelihood ratios with forensic applications, Forensic Science International: Genetics, 2014;9(1):93–101 doi: 10.1016/j.fsigen.2013.11.008

Berger CEH, Buckleton J, Champod C, Jackson G. Evidence evaluation: A response to the court of appeal judgment in R. v. T. Science & Justice, 2011,;51(2):43-9 •

Neumann C, Kaye D, Jackson G, Ranadive A. Presenting Quantitative and Qualitative Information on Forensic Science Evidence in the Courtroom. Chance, 2016; 29(1):37-43

Biedermann A, Taroni F, Bozza S. Implementing statistical learning methods through Bayesian networks. Part 1: A guide to Bayesian parameter estimation using forensic science data. Forensic science international, 2009;193(1-3):63-71 doi: 10.1016/j.forsciint.2009.09.007

Neumann C, Ranadive A, Kaye DH, Reyna V. Communicating the Results of Forensic Science Examinations. Final Technical Report for NIST Award 70NANB12H014, 2015 doi: 10.13140/RG.2.1.2078.8246

Taroni F, Biedermann A, Bozza S. Statistical hypothesis testing and common misinterpretations: Should we abandon p-value in forensic science applications? Forensic Science İnternational, 2016; 259:32-36 • DOI: 10.1016/j.forsciint.2015.11.013

Meuwly D. Forensic individualisation from biometric data, Science & Justice, 2006;46:205-213

Evett IW, Weir SB, Interpreting DNA evidence:Statistical genetics for forensic scientists, Sinauer Associates, Sunderland, MA, 1998

Saini M, Kapoor AK. Conventional and computational features in document examination, Journal of Forensic Science & Criminology, 2015;3:1-7

Desai B, Kalyan JL. Forensic examination of handwriting and sigantures, International Journal of Innovative Research&Development, 2013;2:514-527. Doi: 10.15744/2348-9804.3.301

Hussain R, Raza A, Siddiqi I, Khursid K, Djeddi C. A compherensive survey of handwritten document benchmarks: structure, usage and evalution, Journal on Image and Video Processing, 2015;46:1-24. Doi:10.1186/s13640-015-0102-5

Johnson ME, Vastrick TW, Boulanger M, Schuetzner EM. Measuring the Frequency occurrence of handwriting and hand-printing characteristics, National Institute of Justice, Award Number 2010-DN-BX-K273.

Davis LJ , Saunders CP , Hepler A , Buscaglia J. Using subsampling to estimate the strength of handwriting fevidence via score-based likelihood ratios, Forensic Science International,2012;216:146-157

Raymond M, Bozza S, Schmittbuhl M, Taroni F. Handwriting evidence evaluation based on the shape of characters: Application of multivariate likelihood ratios, Journal of Forensic Sciences, 2011;56:238-242

Hepler AB , Saunders CP, Davis LJ, Buscaglia J. Score-based likelihood ratios for handwriting evidence, Forensic Science International,2012;219:129-140

Srihari SN. Statistical examination of handwriting characteristics using automated tools. NIJ Report 241743, 2013, Award Number: 2010-DN-BX-K037

Srihari SN. Computational methods for handwritten questioned document examination. NIJ Report 232745, 2010, Award Number:2004-IJ-CX-K050

Marquis R, Schmittbuhl M, Mazzella WD, Taroni F. Quantification of the shape of handwritten characters:a step to objective discrimination between writers based on the study of the capital character O, Forensic Science International, 2005;150:23-32

Akkurt M. Adli tıp kurumu fizik ihtisas dairesi adli belge inceleme şubesine gelen ve müzekkere ile iade edilen dosyaların incelenmesi [MSc tezi], Ulusal Tez Merkezi 308084, 2011

How to Cite
Öner D, Cengiz S, Çetin G. Determination of Evidence Evaluation of Individual Characteristics Used in Handwriting Analysis According to the Frequencies in Database. atb [Internet]. 29Apr.2017 [cited 16Jul.2018];22(1):1-3. Available from:
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