Two Staged Fuzzy SVM Algorithm and Beta-Elliptic Model for Online Arabic Handwriting Recognition

Online handwriting recognition has been gaining more interest in the field of document analysis due to the growth of data entry technology. In this context, we propose a new architecture for online Arabic Word recognition based on a pre-classification of their handwriting trajectory segments delimited by pen-down and pen-up actions. To characterize these segments, we extract their kinematic and geometric profiles characteristics according to the overlapped beta-elliptic approach. The main contribution in this work consists on combining two stages of Support Vector Machines (SVM). The first one is developed in fuzzy logic (Fuzzy SVM) and allows computing the membership probabilities of pseudo-words in different sub-groups. The second stage consists on gathering the membership probabilities vectors of pseudo-words belonging to the same word in order to predict the word label. The tests are performed on 937 classes which represent the Tunisian town names from the ADAB database. The obtained results show the effectiveness of the proposed architecture which reached the rate of 99.89%.