Automatic allograph matching in forensic writer identification

Niels, R., Vuurpijl, L. & Schomaker, L. R. B., Feb-2007, In : International Journal of Pattern Recognition and Artificial Intelligence. 21, 1, p. 61-81 21 p.

Research output: Contribution to journalArticleAcademicpeer-review

A well-established task in forensic writer identification focuses on the comparison of prototypical character shapes (allographs) present in handwriting. In order for a computer to perform this task convincingly, it should yield results that are plausible and understandable to the human expert. Trajectory matching is a well-known method to compare two allographs. This paper assesses a promising technique for so-called human-congruous trajectory matching, called Dynamic Time Warping (DTW). In the first part of the paper, an experiment is described that shows that DTW yields results that correspond to the expectations of human users. Since DTW requires the dynamics of the handwritten trace, the "online" dynamic allograph trajectories need to be extracted from the "offline" scanned documents. In the second part of the paper, an automatic procedure to perform this task is described. Images were generated from a large online dataset that provides the true trajectories. This allows for a quantitative assessment of the trajectory extraction techniques rather than a qualitative discussion of a small number of examples. Our results show that DTW can significantly improve the results from trajectory extraction when compared to traditional techniques.

Original languageEnglish
Pages (from-to)61-81
Number of pages21
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number1
Publication statusPublished - Feb-2007
Event12th Conference of the International-Graphonomics-Society - , Italy
Duration: 26-Jun-200529-Jun-2006


12th Conference of the International-Graphonomics-Society



Event: Other


  • forensic writer identification, dynamic time warping, allograph matching, trajectory extraction, CURSIVE SCRIPT, RECOGNITION

ID: 1618576