Paper
Recognition of Handwritten Digits Using Optimized Adaptive Neuro-Fuzzy Inference Systems and Effective Features
- Authors:
- Amir Bahador Bayat
- Abstract
- Automatic recognition of handwritten characters has long been a goal of many research efforts in the pattern recognition field. This paper investigates the design of a high efficient system for recognition of handwritten digits. First it proposes an efficient system that includes two main modules: the feature extraction module and the classifier module. In the feature extraction module, seven sets of discriminative features are extracted and used in the recognition system. In the classifier module, as the first time in this area, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. Experimental results show that the proposed system has good Recognition Accuracy (RA). However, the results show that in ANFIS training, the vector of radius has very important role for its recognition accuracy. At the second fold, it proposes an intelligence system in which a novel optimization module, i.e., improved bees algorithm (IBA) is proposed for finding the best parameters of the classifier. In test stage, 3-fold cross validation method was applied to the MNIST handwritten numeral database to evaluate the proposed system performances. Simulation results show that the proposed system has high recognition accuracy.
- Keywords
- Handwritten Digits; ANFIS; MNIST; IBA; Optimization
- StartPage
- 25
- EndPage
- 37
- Doi