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Sharifah Nur Syafiqah Mohd Nur Hidayah
Universiti Teknologi MARA

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Comparison of convolutional neural network and bag of features for multi-font digit recognition Nasibah Husna Mohd Kadir; Sharifah Nur Syafiqah Mohd Nur Hidayah; Norasiah Mohammad; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1322-1328

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.
Co-Authors Nasibah Husna Mohd Kadir Norasiah Mohammad Zaidah Ibrahim