Research on Uyghur Off-line Handwriting-based Writer Identification

Many techniques have been reported for handwriting-based writer identification, but none of these techniques presented that the written text is in Uyghur. In this paper, we propose a technique for off line writer identification for Uyghur handwriting, Texture features were extracted for wide range of frequency and orientation because of the nature of the Uyghur handwriting. 144 features are extracted from the handwriting sample using 2-D Gabor fillers. By applying feature selection and extraction methods on this set of features, subsets of lower dimensionality are obtained. The most discriminant features were selected with a model for feature selection using genetic algorithm techniques. Three classification techniques were used: support vector machine (SVM), weighted Euclidean distance (WED), and the K nearest neighbours (K_NN) classifier. Experiments were performed using Uyghur handwriting samples from 23 different people and very promising results of 88.0% correct identification rate were achieved.