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Norasiah Mohammad
Universiti Teknologi MARA

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Deep Learning for Roman Handwritten Character Recognition Muhaafidz Md Saufi; Mohd Afiq Zamanhuri; Norasiah Mohammad; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp455-460

Abstract

The advantage of deep learning is that the analysis and learning of massive amounts of unsupervised data make it a beneficial tool for Big Data analysis. Convolution Neural Network (CNN) is a deep learning method that can be used to classify image, cluster them by similarity, and perform image recognition in the scene. This paper conducts a comparative study between three deep learning models, which are simple-CNN, AlexNet and GoogLeNet for Roman handwritten character recognition using Chars74K dataset. The produced results indicate that GooleNet achieves the best accuracy but it requires a longer time to achieve such result while AlexNet produces less accurate result but at a faster rate.
Flower and leaf recognition for plant identification using convolutional neural network Nurul FatihahSahidan; Ahmad Khairi Juha; Norasiah Mohammad; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp737-743

Abstract

This paper presents flower and leaf recognition for plant identification using Convolutional Neural Network (CNN). In this study, the performance of CNN for plant identification using images of the leaves, flowers and a combination of both are investigated.  Two publicly available datasets, namely Folio leaf dataset and Flower Recognition dataset, have been used for the training and testing purposes.  CNN has been proven to produce excellent results for object recognition but its performance can still be influenced by the type of images and the number of layers of the CNN architecture.   Experimental results indicate that the utilization of leaf images only arrive to the highest accuracy for plant identification compared to the images of flowers only or the combination of both, that are 98%, 85% and 74%, respectively.
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 Ahmad Khairi Juha Mohd Afiq Zamanhuri Muhaafidz Md Saufi Nasibah Husna Mohd Kadir Nurul FatihahSahidan Sharifah Nur Syafiqah Mohd Nur Hidayah Zaidah Ibrahim