A Persian writer identification method using swarm-based feature selection approach

Handwriting is one of the most famous biometrics which is processed based on image processing and pattern recognition techniques. However, there are a lot of reports that have already been published on handwritten text identification methods and researchers try to improve the accuracy and speed of such methods. This paper presents an offline Persian handwriting identification method in which some new text features are extracted and best ones are selected using a swarm-based approach. The essence of this feature selection method is bees algorithm, which is a modern swarm-based meta-heuristic approach. In the proposed technique, the adaptive neuro-fuzzy inference system (ANFIS) is employed as classifier and trained by the input feature vectors. It is also compared with a multi-layer perceptron (MLP) and fuzzy K-nearest neighbour classifiers. To test the proposed method, we have collected a handwritten Persian text dataset from 125 people who have written six sheets with five lines in each of optional Persian texts. Experimental results showed that the prediction accuracy was about 98% in average while the method training time is less than most related works. It seems this method can be extended for other languages by adjusting its parameters.