Arabic handwriting recognition using Curvelet transform and SVM

Abstract

Arabic cursive language recognition is an ever challenging problem in OCR applications. Traditional approaches to tackle this problem fail to adapt to the vast variability imposed by handwritten Arabic language, this necessitate the devising of more holistic techniques. Recent approaches to solve this challenge are making use of multidimensional analysis like wavelet and curvelet for feature extraction and then apply machine learning techniques for recognition. In this project we investigate the use of one of this approaches for feature extraction by applying Curvelet Transform to profile curvatures present in words without character segmentation mimicking the human way of recognition. The IFN/ENIT database of Tunisian towns is used and we apply SVM multi-classification for training a modal to intelligently classify those words. Results showed an accuracy of 66% though this accuracy can be elevated by following a certain train/test separation scheme.