Journal of Intelligent Learning Systems and Applications

Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
www.scirp.org/journal/jilsa
E-mail: jilsa@scirp.org
"MCS HOG Features and SVM Based Handwritten Digit Recognition System"
written by Hamayun A. Khan,
published by Journal of Intelligent Learning Systems and Applications, Vol.9 No.2, 2017
has been cited by the following article(s):
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  • CrossRef
[1] An Efficient CNN Model for Automated Digital Handwritten Digit Classification
2021
[2] Un Système de Reconnaissance de Formes Basé sur une Approche Multi-Classifieurs
2021
[3] A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient
2020
[4] Handwritten Bengali Character Recognition Using Deep Convolution Neural Network
2020
[5] Hand Written Digit Recognition using Machine Learning
2020
[6] A Robust Handwritten Digit Recognition System Based on Sliding window with Edit distance
2020
[7] Image Classification using HOG and LBP Feature Descriptors with SVM and CNN
2020
[8] Handwritten Numeral Superposition to Printed Form Using Deep Auto-Encoder and Recognition Using Convolutional Neural Network
2019
[9] Pengenalan Tanda Tangan Menggunakan Histogram Of Oriented Gradients Dan Smooth Support Vector Machine
2019
[10] Recognition of Handwritten Characters based on Deep Learning with Tensor Flow
2019
[11] Fashion-MNIST Classification Based on HOG Feature Descriptor Using SVM
International Journal of Innovative Technology and Exploring Engineering, 2019
[12] Hybrid CNN-GRU model for high efficient handwritten digit recognition
2019
[13] Recognition of Handwritten Characters based on Deep Learning with Tensorflow
2019
[14] HANDWRITING RECOGNITION AND PREDICTION USING STOCHASTIC LOGISTIC REGRESSION
2018
[15] Efficient Handwritten Digit Classification using User-defined Classification Algorithm
2018
[16] Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers
2018
[17] Bengali handwritten character recognition using deep convolutional neural network
2017
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