Open AccessHybrid Firefly Algorithm with Artificial Neural Network (FA-ANN) Classification Model for Handwritten Character Recognition
Author(s) -
Maziah Mohamad,
H. Haron
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012088
Subject(s) - artificial neural network , firefly algorithm , character (mathematics) , computer science , artificial intelligence , pattern recognition (psychology) , neocognitron , time delay neural network , machine learning , nist , speech recognition , mathematics , geometry , particle swarm optimization
This paper acknowledged the issues regarding HCR performances particularly in the classification stage. It is generally agreed that one of the main factors influencing performance in HCR is the development of classification model. As for the classification stage, the problems identified are related to classification model particularly in Artificial Neural Network (ANN) learning problem that results in low accuracy of handwritten character recognition. Thus, the aim of this study is to develop and enhance the ANN classification model in order to identify the handwritten character better. This paper proposed the hybrid Firefly Algorithm with Artificial Neural Network (FA-ANN) classification model for handwritten character. Firefly algorithm acts as optimisation approach in enhancing ANN particularly by optimize network training process of ANN. National Institute of Standards and Technology (NIST) handwritten character database was applied in the experiment.