Abstract
Handwriting is considered to be one of the commonly used biometric modality to identify persons in forensic application. In this paper, we propose a new and efficient approach for off-line text-independent writer identification. Indeed, complete section of connected ink race (Connected-Component or CC), delimited only by where the writer has lifted the pen is inspected in this approach relatively to its aspects of directionality, angularity and length. A set of features is extracted from pre-processed CCs to characterize the writer of a handwritten sample. In order to enhance the performance of our writer identification system, we have combined the features. Experiments are made using handwritings from different people of two datasets, the IFN/ENIT dataset which consists of 1750 documents from 350 writers, and the ICDAR2013 dataset which contains 1000 documents from 250 writers. Our approach showed better properties than most of the surveyed techniques in terms of supported corpus size and identification rate.
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 callMore From: International Review on Computers and Software (IRECOS)
Disclaimer: 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.
