Title of article :
Off-Line Arabic Handwritten Word Segmentation Using Rotational Invariant Segments Features
Author/Authors :
Abdulla, Shubair Ajman University of Science and Technology - Faculty of Education and Basic Sciences, UAE , Al-Nassiri, Amer Ajman University of Science and Technology - Faculty of IT, UAE , Abdul Salam, Rosalina University SaMalaysia - School of Computer Sciences, Malaysia
Abstract :
This paper describes a new segmentation algorithm for handwritten Arabic characters using Rotational Invariant Segments Features (RISF). The algorithm evaluates a large set of curved segments or strokes through the image of the input Arabic word or subword using a dynamic feature extraction technique then nominates a small “optimal” subset of cuts for segmentation. All the directions of stroke are converted to two main segments: + and w - RISF. A list of nominated segmentation points are prepared from the + segments and evaluated according to special conditions to locate the final segmentation points. The RISF algorithm was tested by using our new designed database AHD/AUST and the IFN/ENIT database. It has achieved a high segmentation rate of 95.66% on AHD/AUST and 90.58% on IFN/ENIT handwritten Arabic databases.
Keywords :
Feature extraction , Arabic character segmentation , cursive writing , Arabic words database
Journal title :
The International Arab Journal of Information Technology (IAJIT)
Journal title :
The International Arab Journal of Information Technology (IAJIT)