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

Nasibah Husna Mohd Kadir (Universiti Teknologi MARA)
Sharifah Nur Syafiqah Mohd Nur Hidayah (Universiti Teknologi MARA)
Norasiah Mohammad (Universiti Teknologi MARA)
Zaidah Ibrahim (Universiti Teknologi MARA)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 15, No 3: September 2019

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.