Performance optimization of neural networks in handwritten digit recognition using Intelligent Fuzzy C-Means clustering

Author

Miri, E. ; Razavi, Seyed Mohsen ; Sadri, J.

Author_Institution

Dept. of Electr. Eng., Univ. of Birjand, Birjand, Iran

fYear

2011

fDate

13-14 Oct. 2011

Firstpage

150

Lastpage

155

Abstract

In this paper, a new approach has been proposed in order to optimize performance of Multi Layer Perceptron Neural Networks in handwritten digit recognition. In the proposed approach, Fuzzy C-Means clustering with PSO optimizer has been used, and it has been applied in handwritten Farsi digits recognition. Obtained results show that with the help of this approach we can reduce the rate of misclassifications as compared to other common approaches found in the literature.

Keywords

fuzzy set theory; handwritten character recognition; multilayer perceptrons; natural language processing; optimisation; pattern clustering; performance evaluation; PSO optimizer; handwritten Farsi digit recognition; intelligent fuzzy C-mean clustering; multilayer perceptron neural networks; neural networks; performance optimization; Feature extraction; Handwriting recognition; Neural networks; Neurons; Training; Vectors; Fuzzy C-Means (FCM) Clustering; Multi Layer Perceptron (MLP) Neural Network; OCR; PSO; Pattern Recognition; Recognition of Farsi Handwritten Digits;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on

Conference_Location

Mashhad

Print_ISBN

978-1-4673-5712-8

Type

conf

DOI

10.1109/ICCKE.2011.6413342

Filename

6413342