Comparison of convolutional neural network and bag of features for multi-font digit recognition

2Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text. BoF is a popular machine learning method while CNN is a popular deep learning method. Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.

Cite

CITATION STYLE

APA

Kadir, N. H. M., Hidayah, S. N. S. M. N., Mohammad, N., & Ibrahim, Z. (2019). Comparison of convolutional neural network and bag of features for multi-font digit recognition. Indonesian Journal of Electrical Engineering and Computer Science, 15(3), 1322–1328. https://doi.org/10.11591/ijeecs.v15.i3.pp1322-1328

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free