UTSig: A Persian Offline Signature Dataset

The crucial role of datasets in signature verification systems has motivated researchers to collect signature samples. However, with regard to the distinct characteristics of Persian signature, existing offline signature datasets cannot be used in Persian systems. This paper presents a new and public Persian offline signature dataset, UTSig, which consists of 8280 images from 115 classes that each class has 27 genuine, 3 opposite-hand signatures of the genuine signer, and 42 skilled forgeries made by 6 forgers from 230 people. Compared to the other public datasets, UTSig has larger number of samples, classes, and forgers. Meanwhile its samples were collected by considering variables such as signing period, writing instrument, signature box size, and number of observable samples for forgers. Reviewing the main characteristics of offline signature datasets, we statistically show that Persian signatures has fewer number of branch points and end points. We propose and test four different training and testing setups for UTSig. Results of our experiments show that training genuine samples along with opposite-hand signed samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio which is an information theoretic criterion.