Abstract
This paper proposes a system for text‐dependent writer identification based on Arabic handwriting. First, a database of words was assembled and used as a test base. Next, features vectors were extracted from writers′ word images. Prior to the feature extraction process, normalization operations were applied to the word or text line under analysis. In this work, we studied the feature extraction and recognition operations of Arabic text on the identification rate of writers. Because there is no well‐known database containing Arabic handwritten words for researchers to test, we have built a new database of offline Arabic handwriting text to be used by the writer identification research community. The database of Arabic handwritten words collected from 100 writers is intended to provide training and testing sets for Arabic writer identification research. We evaluated the performance of edge‐based directional probability distributions as features, among other characteristics, in Arabic writer identification. Results suggest that longer Arabic words and phrases have higher impact on writer identification.
Highlights
Two fundamental concepts are considered to be critical to writer identification: no two people write exactly alike, and no one person writes exactly the same way twice
This paper presents the problem of automatic writer identification using scanned images of Arabic handwriting
There are four steps in any writer identification system: (i) a step in which samples of scanned handwriting are entered into the system; (ii) a preprocessing step, in which information is set up that will be used to correctly perform the writer identification; (iii) a feature extraction step, which is used to obtain a relevant representation for the last step; (iv) a classification process step, which is the final step of the system
Summary
Two fundamental concepts are considered to be critical to writer identification: no two people write exactly alike, and no one person writes exactly the same way twice. These two principles, oversimplified and disputable, clearly highlight two factors that directly conflict when attempting to identify a person based on handwriting samples. Writer identification is used in forensic and biometric applications, in which the writer of a document can be identified based on handwriting samples. This paper presents the problem of automatic writer identification using scanned images of Arabic handwriting. Journal of Electrical and Computer Engineering possible applications involve the detection of the various handwritings present in a document or the dating of documents compared to the chronology of the author’s work
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 call
