  The pivotal role of datasets in signature verification systems motivates
researchers to collect signature samples. Distinct characteristics of Persian
signature demands for richer and culture-dependent offline signature datasets.
This paper introduces a new and public Persian offline signature dataset,
UTSig, that consists of 8280 images from 115 classes. Each class has 27 genuine
signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6
forgers. Compared with the other public datasets, UTSig has more samples, more
classes, and more forgers. We considered various variables including signing
period, writing instrument, signature box size, and number of observable
samples for forgers in the data collection procedure. By careful examination of
main characteristics of offline signature datasets, we observe that Persian
signatures have fewer numbers of branch points and end points. We propose and
evaluate four different training and test setups for UTSig. Results of our
experiments show that training genuine samples along with opposite-hand samples
and random forgeries can improve the performance in terms of equal error rate
and minimum cost of log likelihood ratio.
