{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/utsig-a-persian-offline-signature-dataset","title":"UTSig: A Persian Offline Signature Dataset","arxiv_id":"1603.03235","date":"2016-03-10","proceeding":null,"authors":["Amir Soleimani","Kazim Fouladi","Babak N. Araabi"],"abstract":"The pivotal role of datasets in signature verification systems motivates\nresearchers to collect signature samples. Distinct characteristics of Persian\nsignature demands for richer and culture-dependent offline signature datasets.\nThis paper introduces a new and public Persian offline signature dataset,\nUTSig, that consists of 8280 images from 115 classes. Each class has 27 genuine\nsignatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6\nforgers. Compared with the other public datasets, UTSig has more samples, more\nclasses, and more forgers. We considered various variables including signing\nperiod, writing instrument, signature box size, and number of observable\nsamples for forgers in the data collection procedure. By careful examination of\nmain characteristics of offline signature datasets, we observe that Persian\nsignatures have fewer numbers of branch points and end points. We propose and\nevaluate four different training and test setups for UTSig. Results of our\nexperiments show that training genuine samples along with opposite-hand samples\nand random forgeries can improve the performance in terms of equal error rate\nand minimum cost of log likelihood ratio.","url_abs":"http://arxiv.org/abs/1603.03235v4","url_pdf":"http://arxiv.org/pdf/1603.03235v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"utsig-a-persian-offline-signature-dataset","repo_url":"https://github.com/parsa-abbasi/IUST-Pattern-Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"siamese-network","method_name":"Siamese Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}