Persian Handwritten Recognition Using Centroid Sequence Freeman Chain Code (CSFCC) Feature and Enhanced Classification

In this paper, a new method is proposed for offline handwritten Persian words recognition. The proposed method introduces the Centroid Sequence Freeman Chain Code (CSFCC) as a new and powerful feature along with the use of morphological features and an optimize support vector machine (SVM) classifier. A conbination of particle swarm optimization (PSO) and gravitational search algorithm (GSA), abbreviated to PSOGSA, has been employed to optimaze the SVM classifier. In the proposed method, all the connected components of a word are detected and combined with each other. For this purpose, a pictorial dictionary of asymptomatic subwords has been made. In addition, a database has been created to include the positions of asymptomatic subwords in order to narrow down the search space and increase the speed and improve the recognition accuracy. Based on the position of a subword in a word, it is more likely to make the right decision and detect the subword, accurately. The proposed method was implemented on the Iranshahr Database, containing nearly 17000 images of handwritten names of 503 cities of Iran. The resultant recognition accuracy is 89% in the expriments, which shows the capability of the proposed method and improving the results, compared to the other well-known methods.