Abstract
A well-established task in forensic writer identification is the comparison of prototypical character shapes (allographs) present in the handwriting. Using elastic matching techniques like Dynamic Time Warping (DTW), comparison results can be made that are plausible and understandable to the human expert. Since these techniques require the dynamics of the handwritten trace, the 'online' dynamic allograph trajectories need to be extracted from the 'offline' scanned documents. We have implemented an algorithm that can automatically extract this information from scanned images. The algorithm makes a list of all possible trajectories. Using a number of traditional techniques and DTW for evaluation, the best trajectory is selected. To be able to make a quantitative assessment of our techniques, rather than a qualitative discussion of a small number of examples, we tested the performance on two large datasets, one containing online and the other containing offline data. Two different methods (one for the online, and one for the offline dataset) are used to validate the generated trajectories. The results of the experiments show that DTW can significantly improve the performance of trajectory extraction when compared to traditional techniques
