Online Arabic Handwriting Recognition Based on Classifier Combination
نویسندگان
چکیده
Handwriting recognition is a rich and complex issue. Some of its problems include the large shape variations in human handwriting. Classifier combination contributes in increasing the classification accuracy compared to the performance of individual classifier. In this paper, we present an online handwriting recognizer based on classifier combination according to holistic approach. We propose two combination types: a combination between online recognition and offline recognition, and a combination between dynamic approach, structural approach and statistical approach. For feature extraction phase and classification phase, we use Point Features (PF) and Dynamic Time Warping (DTW) in dynamic approach, Freeman Chain code (FC) and Levenshtein Distance (LD) in structural approach, Zernike Moments (ZM) and Support Vector Machine (SVM) in statistical approach. In the combination phase, different methods are applied on the results provided by the three classifiers and different combinations are studied. The proposed framework is tested on ADAB database [6]. Keywords—Arabic handwriting; online recognition; offline recognition; classifier combination; holistic approach
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