A robust SIFT feature for fast offline arabic words classification

This paper presents the effectiveness of perceptual features and iterative classification approach for offline Arabic word images classification. Optimum word image feature extraction is the system which can obtain the minimum feature that completely represents the target for matching or classification. In this paper we develop the Arabic word image classification by extracting the feature in three main steps: firstly Scales Invariant Feature Transformation (SIFT) is applied after preprocessing. Secondly important points were selected from the descriptors by using locale maxima operation to the SIFT feature matrix. At last we use linear classifier recognizer. Our proposed approach not only performs well and effectively but also was faster when applied to big database images.