Towards a novel biometric system for forensic document examination

Biometric systems have demonstrated an important enhancement in writer identification from online handwriting. Indeed, writer identification is still a challenging task in the definition of a set of features able to characterize the different samples of handwriting documents. These samples are not usually stable and show a vast variability from the same writer over time, or from different writers. The ability to identify the documents’ authors provides more chances for using these documents for various purposes and especially in forensic analysis. In this paper, we present a biometric based recognition system for forensic document examination capable of identifying a document's author. This system consists of the preprocessing and the segmentation of online handwriting into a sequence of strokes in a first step. Then, from each stroke, we extract a set of static and dynamic features. Next, all the segments which are composed of two consecutive strokes are categorized into groups and subgroups according to their position and their geometric characteristics. Finally, Deep Neural Network is used as a classifier. Experiments which are conducted on IBM_UB_1, IAM OnDB, NLPR Handwriting and ADAB databases, demonstrate that the proposed system outperforms the existing writer identification systems on Latin and Arabic scripts. This justifies its usefulness in forensic examination of handwriting.