نوع مقاله : پژوهشی
1 دانشکده مهندسی صنایع- دانشگاه صنعتی شریف
2 دانشکده مهندسی صنایع-دانشگاه صنعتی شریف
عنوان مقاله [English]
Pattern recognition is a branch of machine learning that recognizes the patterns and regularities in a set of data and digit recognition is considered as one of the categories of pattern recognition. Due to the similarities between some digits in each language and especially in Persian, different algorithms have been developed to recognize the handwriting digits with the least error and in the shortest time complexity. One of the most common used methods in data classification is the neural network algorithm. while neural networks have been used in the literature for handwriting digits recognition, the combination of clustering approaches and neural network classifiers has not been considered for this problem. Accordingly, in this paper, an algorithm based on the combination of clustering approaches and neural network classifiers is proposed to recognize accurately the Persian handwritten digits. In this algorithm, the pattern training and recognition are performed based on probabilistic neural networks (PNN) and multilayer perceptron (MLP) neural networks. In this regard, after extracting the characteristic loci feature and zoning from each image in the training database, the data of each of the ten classes has been clustered using linkage, Partition Around Medoids (PAM) and Fuzzy C-Means (FCM) methods based on the extracted features. Then, the new ten classes resulted from the clustering algorithm are taught by one of the two classifiers including MLP and PNN. In order to determine the optimal number of clusters in each class, the Tabu search optimization algorithm which is one of the most accurate meta-heuristic optimization algorithms is used. The performance of the proposed algorithms is evaluated and compared with existing algorithms based on HODA dataset. Based on the results, the proposed algorithm recognizes accurately the Persian handwritten digits. In addition, the proposed method performs more accurate and much faster than most of the competing algorithms.