Abstract
Writer Identification has gained increasing importance in the scientific community in recent years. In this paper, we propose an approach based on the combination of local textural descriptors and encoding methods (VLAD and Triangulation Embedding). The tests carried out in the bilingual LAMIS dataset made it possible to reach 100% in the Arabic version and 100% in the French version.
Highlights
IntroductionHandwriting is a characteristic that differs from person to other person. it represents a biometric tool that develops from birth
In biometrics which is based on human handwriting, we are concerned with authenticating people using the behavioral aspects of their writing style [10]
The two approaches of writer identification and writer verification are considered to be the main pillars in the work which is interested in the study of writing styles in the field of biometrics [15]
Summary
Handwriting is a characteristic that differs from person to other person. it represents a biometric tool that develops from birth. In [7], the authors used textural descriptors like Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) computed on small image patches. Their system has achieved an identification rate of 94.89% on the IFN/ENIT dataset. We will propose an approach based on the computation of Linear Local Binary Pattern histograms in both horizontal and vertical directions. These histograms are calculated at the level of small patches of sizes 15x15. – Propose a Writer Identification system based on Linear Local Binary Pattern textural descriptors. – Check the impact of clustering on the classification results. – Carry out the tests in two subsets of the bilingual LAMIS dataset
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
