Assessing the authorship confidence of handwritten items

Abstract

The Writer Verification is a process to compare questioned handwriting with samples of handwriting obtained from known sources for the purposes of determining authorship or non-authorship. It plays an important investigative and forensic role in many types of crime. Particularly problematic in this regard is the fact that there exists neither true standard nor universal definition of comparison. For this reason, we propose an algorithmic objective approach. Using visual information of two or more digitally scanned handwritten items, we show a method to access the authorship confidence that is the probability of errors. Instead of building a costly handwritten item database to support the confidence, questioned words are simulated from the CEDAR letter image database in order to handle any handwritten items. An Artificial Neural Network is trained to verify the authorship using the synthesized words.