Open AccessImproving Arabic writer identification using score‐level fusion of textural descriptors
Author(s) -
Hannad Yaâcoub,
Siddiqi Imran,
Djeddi Chawki,
ElKettani Mohamed ElYoussfi
Publication year - 2019
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2018.5009
Subject(s) - handwriting , computer science , arabic , histogram , identification (biology) , artificial intelligence , natural language processing , fusion , similarity (geometry) , pattern recognition (psychology) , database , image (mathematics) , linguistics , philosophy , botany , biology
This paper investigates the problem of writer identification from handwriting samples in Arabic. The proposed technique relies on extracting small fragments of writing which are characterised using two textural descriptors, Histogram of Oriented Gradients (HOG) and Gray Level Run Length (GLRL) Matrices. Similarity scores realised using HOG and GLRL features are combined using a number of fusion rules. The system is evaluated on three well‐known Arabic handwriting databases, the IFN/ENIT database with 411 writers, the KHATT database with 1000 writers, and QUWI database with 1,017 writers. Fusion using the ‘sum’ rule reports the highest identification rates reading 96.86, 85.40, and 76.27% on IFN/ENIT, KHATT, and QUWI databases, respectively. The results realised on the KHATT database are comparable to the state of the art while those reported on the IFN/ENIT and QUWI databases are the highest to the best of authors' knowledge.