classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

Letter recognition and handwritten processing is one of the major and open problems in Artificial Intelligent (AI) domain. This study introduces a method based on statistical and geometrical techniques to recognize handwritten digits and letters. These techniques use the fuzzy logic to create the vector curves. Inputs are online digits or letters and outputs are two arrays of slope and curvatur...

Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemble method. Furthermore, the feature selection is used to reduce the costs of ...

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...

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...

This paper discusses a methodology for handwritten character recognition applying feature subset selection to reduce number of features. Its novelty lies in the use of a genetic algorithm for the preparation of input data for a support vector machine which is employed to recognize the handwritten Persian digits in particular. Comprehensive experiments on handwritten Persian digits demonstrate t...

Persian handwritten numerals recognition has been a frontier area of research for the last few decades under pattern recognition. Recognition of handwritten numerals is a difficult task owing to various writing styles of individuals. A robust and efficient method for Persian/Arabic handwritten numerals recognition based on K Nearest Neighbors (K-NN) classifier is presented in this paper. The sy...

This paper discusses the use of fast and customized Dynamic Time Warping method for offline Persian handwriting recognition that could be easily extended to Arabic language. Many systems in this field are based on either Neural Network or Hidden Markov Model that suffer from low recognition rate, sensitivity to noises or wide range of parameters that reduce system performance. The complete syst...