title: Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach.
abstract: The presence of a large number of unique shapes called ligatures in cursive languages, along with variations due to scaling, orientation and location provides one of the most challenging pattern recognition problems. Recognition of the large number of ligatures is often a complicated task in oriental languages such as Pashto, Urdu, Persian and Arabic. Research on cursive script recognition often ignores the fact that scaling, orientation, location and font variations are common in printed cursive text. Therefore, these variations are not included in image databases and in experimental evaluations. This research uncovers challenges faced by Arabic cursive script recognition in a holistic framework by considering Pashto as a test case, because Pashto language has larger alphabet set than Arabic, Persian and Urdu. A database containing 8000 images of 1000 unique ligatures having scaling, orientation and location variations is introduced. In this article, a feature space based on scale invariant feature transform (SIFT) along with a segmentation framework has been proposed for overcoming the above mentioned challenges. The experimental results show a significantly improved performance of proposed scheme over traditional feature extraction techniques such as principal component analysis (PCA).
authors.affiliation: University of Technology, Kaiserslautern, Germany; Shaheed Benazir Bhutto University, Sheringal, Pakistan.
authors.forename: Riaz
authors.initials: R
authors.lastname: Ahmad
authors.affiliation: Hazara University, Department of IT, Mansehra, Pakistan; GGPGC No.1, Abbottabad, Pakistan.
authors.forename: Saeeda
authors.initials: S
authors.lastname: Naz
authors.affiliation: University of Technology, Kaiserslautern, Germany.
authors.forename: Muhammad Zeshan
authors.initials: MZ
authors.lastname: Afzal
authors.affiliation: Genie Technologies (Pvt) Ltd. Lahore, Pakistan.
authors.forename: Sayed Hassan
authors.initials: SH
authors.lastname: Amin
authors.affiliation: University of Technology, Kaiserslautern, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Breuel
mesh_terms: D000465:Algorithms; D007802:Language; D008877:Middle East; D010363:Pattern Recognition, Automated / Q000379:methods*; D014956:Writing