Persian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
نویسندگان
چکیده
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 Persian handwritten digit images, has been used to evaluate our proposed classifier. Obtained results show that PNN is a powerful classifier and excellent choice for classification of Persian handwritten digits. Correct recognition rate when training and testing data have been used directly (without clustering) for training data is 100% and for testing data is 96%, but when k-means has been used as cluster tool and clusters' center have been used as training data, in this case, correct recognition rate for training data is 100% and for testing data is 96.16%. In addition, when Particle Swarm Optimization (PSO) has been used to find optimum clusters for each class of Persian handwritten digits, correct recognition rate in training data is 100% and for the testing data it
منابع مشابه
Persian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...
متن کاملApplication of Support Vector Machines for Recognition of Handwritten Arabic/Persian Digits
A new method for recognition of isolated handwritten Arabic/Persian digits is presented. This method is based on Support Vector Machines (SVMs), and a new approach of feature extraction. Each digit is considered from four different views, and from each view 16 features are extracted and combined to obtain 64 features. Using these features, multiple SVM classifiers are trained to separate differ...
متن کاملPersian handwritten digits recognition: A divide and conquer approach based on mixture of MLP experts
In pursuit of Persian handwritten digit recognition, many machine learning techniques have been utilized. Mixture of experts (MOE) is one of the most popular and interesting combining methods which has great potential to improve performance in machine learning. In MOE, during a competitive learning process, the gating networks supervise dividing input space between experts and experts obtain sp...
متن کاملA Probabilistic Neural Network to Recognize Handwritten Digits using Boundary Descriptor Properties
Recognition of handwritten digits is a challenging task, because the writers may possibly write with dissimilar styles, sizes, width and shapes. A probabilistic neural network for recognizing handwritten digits is proposed here. Normalization of the digits of varying sizes is done for getting better boundary descriptor properties. The different boundary descriptor features extracted for recogni...
متن کاملA PSO-Based Modified Counterpropagation Neural Network Model for Online Handwritten Character Recognition
Online handwriting recognition today has special interest due to increased usage of the hand held devices and it has become a difficult problem because of the high variability and ambiguity in the character shapes written by individuals. One major problem encountered by researchers in developing character recognition system is selection of efficient features (optimal features). In this paper, P...
متن کامل