عنوان المقالة: Handwritten signature identification based on MobileNets model and support vector machine classifier
أ.م.د مصطفى سلام كاظم | Mustafa Salam Kadhm | 15679
نوع النشر
مجلة علمية
المؤلفون بالعربي
المؤلفون بالإنجليزي
Israa Bashir Mohammed, Bashar Saadoon Mahdi, Mustafa Salam Kadhm
الملخص الانجليزي
Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.
تاريخ النشر
01/08/2023
الناشر
Bulletin of Electrical Engineering and Informatics
رقم المجلد
12
رقم العدد
4
رابط DOI
https://doi.org/10.11591/eei.v12i4.4965
الصفحات
2401-2409
رابط خارجي
https://beei.org/index.php/EEI/article/view/4965
الكلمات المفتاحية
CEDAR; Handwritten signatures; MobileNets; Support vector machine
رجوع