An Optimized Deep Learning Model for Handwriting Verification

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Afzali P.; Rezapour A.; Rezaee Jordehi A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF OPERATIONAL RESEARCH IN ITS APPLICATIONS Year:2024 | Volume:21 | Issue:1 پیاپی (80) Page(s): 1-16

Download Full-Text

Persian Verion

View:

129

Download:

Cites:

Information Journal Paper

Title

An Optimized Deep Learning Model for Handwriting Verification

Author(s)

Afzali P. | Rezapour A. | Rezaee Jordehi A. | Issue Writer Certificate

Keywords

Writer Verification

Handwriting

Siamese Network

Transfer Learning

Gray Wolf Optimizer Algorithm

Abstract

Handwriting is known as a common way of communication among human societies and a behavioral biometric for authentication. Writer verification of a handwritten text has significant applications in the field of security systems, forensics and historical and literary documents. In this paper, for the first time, an optimized deep architecture utilizing Siamese network, transfer learning and gray wolf optimization algorithm is presented in order to offline writer verification from Persian handwriting. Due to the lack of access to an appropriate dataset in Persian language, a dataset including Persian words, sentences and digits has been collected. In addition, two well-known datasets of IAM and KHATT were also used to analyze the proposed method. The results of experiments and evaluations on these datasets emphasize the ability of the proposed approach to writer verification from handwriting in different languages and various writing styles.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans