Development of English Handwritten Recognition Using Deep Neural Network

Teddy Surya Gunawan, Ahmad Fakhrur Razi Mohd Noor, Mira Kartiwi


Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and EMNIST, were selected to provide dataset for training and testing phase of DNN. Altogether, there are 10 digits [0-9] and 52 letters [a-z, A-Z]. The proposed DNN used stacked two autoencoder layers and one softmax layer. Recognition accuracy for English digits and letters is 97.7% and 88.8%, respectively. Performance comparison with other structure of neural networks revealed that the weighted average recognition rate for patternnet, feedforwardnet, and proposed DNN were 80.3%, 68.3%, and 90.4%, respectively. It shows that our proposed system is able to recognize handwritten English digits and letters with high accuracy.


Handwritten Recognition; Deep Neural Network; Neural Network; MNIST; EMNIST

Full Text:




  • There are currently no refbacks.

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

shopify stats IJEECS visitor statistics