DT2F-TLNet: A novel text-independent writer identification and verification model using a combination of deep type-2 fuzzy architecture and Transfer Learning networks based on handwriting data

Yang，Jing1; Shokouhifar，Mohammad2; Yee，Por Lip1; Khan，Abdullah Ayub3; Awais，Muhammad4; Mousavi，Zohreh5,6

2024-05-15

10.1016/j.eswa.2023.122704

Expert Systems with Applications   影响因子和分区

0957-4174

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches.

Deep Neural Network Feature learning Handwriting Transfer learning Type-2 fuzzy Writer identification

SCI ; EI

英语

其他

ENGINEERING

2-s2.0-85178160156

Scopus

1.Department of Computer System and Technology,Faculty of Computer Science and Information Technology,Universiti Malaya,Kuala Lumpur,50603,Malaysia
2.Department of Electrical & Computer Engineering,Shahid Beheshti University,Tehran,1983969411,Iran
3.Department of Computer Science and Information Technology,Benazir Bhutto Shaheed University Lyari,Karachi,75660,Pakistan
4.Department of Creative Technologies,Air University Islamabad,Pakistan
5.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,China
6.Department of Mechanical Engineering,University of Tabriz,Tabriz,Iran

Yang，Jing,Shokouhifar，Mohammad,Yee，Por Lip,et al. DT2F-TLNet: A novel text-independent writer identification and verification model using a combination of deep type-2 fuzzy architecture and Transfer Learning networks based on handwriting data[J]. Expert Systems with Applications,2024,242.

Yang，Jing,Shokouhifar，Mohammad,Yee，Por Lip,Khan，Abdullah Ayub,Awais，Muhammad,&Mousavi，Zohreh.(2024).DT2F-TLNet: A novel text-independent writer identification and verification model using a combination of deep type-2 fuzzy architecture and Transfer Learning networks based on handwriting data.Expert Systems with Applications,242.

Yang，Jing,et al."DT2F-TLNet: A novel text-independent writer identification and verification model using a combination of deep type-2 fuzzy architecture and Transfer Learning networks based on handwriting data".Expert Systems with Applications 242(2024).