Vietnamese handwritten character recognition using convolutional neural network

Truong Quang Vinh, Le Hoai Duy, Nguyen Thanh Nhan


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 handwriting character recognition by Convolutional Neural Networks (CNNs) – a kind of deep neural network model can achieve high performance on hard recognition tasks. The proposed architecture of the CNN network for Vietnamese handwriting 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.


Handwriting recognition, Convolutional Neural Network, Overfitting problem, Vietnamese handwriting

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

View IJAI Stats