Vietnamese handwritten character recognition using convolutional neural network

Abstract

Handwriting recognition is one of the core applications of computer vision for real-word problems and it has been gaining more interest because of the progression in this field. This paper presents an efficient model for Vietnamese handwritten character recognition by Convolutional Neural Networks (CNNs) – a kind of deep neural network model which can achieve high performance on hard recognition tasks. The proposed architecture of the CNN network for Vietnamese handwritten character recognition consists of five hidden layers in which the first 3 layers are convolutional layers and the last 2 layers are fully-connected layers. Overfitting problem is also minimized by using dropout techniques with the reasonable drop rate. The experimental results show that our model achieves approximately 97% accuracy.