An Applied Method to Online Recognition of Farsi Handwritten Isolated Characters Using Knowledge of Main Body and Tiny Movements Simultaneously

In this paper, a method is presented to online Farsi handwritten isolated characters. In the proposed method, the information of main body and tiny movements are simultaneously used to improve the validation of output class recognition. Farsi handwritten isolated characters are categorized in 18 groups based on similarity in main body and also 11 groups based on tiny movement. According to the proposed method in this paper, the main body and tiny movements are recognized to identify unknown input characters. If detected groups from main body and tiny movements are corresponded, the unknown character is recognized; otherwise this mistake will be corrected by correction algorithm, as much as possible. In this paper, point features and global features are extracted from main body. Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are applied to reduce computational burden and to increase the quality of features. Using PCA and LDA, feature dimension is reduced from 102 to 17 for main body. One Versus One (OVO) approach of Support Vector Machine (SVM) classifier is used to classify the main body of characters and also tiny movements. The obtained results show that by using the proposed method; about 98 percent of online Farsi handwritten isolated characters are correctly recognized.