MCS HOG Features and SVM Based Handwritten Digit Recognition System

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Journal of Intelligent Learning Systems and Applications

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ISSN Online: 2150-8410
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"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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[4]	Analysis of Deep Learning Techniques for Handwritten Digit Recognition
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[5]	Handwritten numeral recognition through superimposition onto printed form
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[7]	Un Système de Reconnaissance de Formes Basé sur une Approche Multi-Classifieurs
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[8]	A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient
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[9]	Handwritten Bengali Character Recognition Using Deep Convolution Neural Network
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[10]	Hand Written Digit Recognition using Machine Learning
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[11]	A Robust Handwritten Digit Recognition System Based on Sliding window with Edit distance
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[12]	Image Classification using HOG and LBP Feature Descriptors with SVM and CNN
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[13]	Handwritten digit recognition using various machine learning algorithms and models
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[14]	Handwritten Numeral Superposition to Printed Form Using Deep Auto-Encoder and Recognition Using Convolutional Neural Network
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[15]	Pengenalan Tanda Tangan Menggunakan Histogram Of Oriented Gradients Dan Smooth Support Vector Machine
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[16]	Recognition of Handwritten Characters based on Deep Learning with Tensor Flow
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[17]	Fashion-MNIST Classification Based on HOG Feature Descriptor Using SVM
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[18]	Hybrid CNN-GRU model for high efficient handwritten digit recognition
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[19]	HANDWRITING RECOGNITION AND PREDICTION USING STOCHASTIC LOGISTIC REGRESSION
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[20]	Efficient Handwritten Digit Classification using User-defined Classification Algorithm
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[21]	Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers
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[22]	Bengali handwritten character recognition using deep convolutional neural network
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[23]	HANDWRITTEN NUMBER ANALYSIS AND PREDICTION OF NUMBERS


[24]	HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING ALGORITHM


[25]	Comparative Analysis of Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms
	RPK Reddy

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[2]	Evolutionary Computing and Mobile Sustainable Networks Lecture Notes on Data Engineering and Communications Technologies, 2022 DOI:10.1007/978-981-16-9605-3_8

[3]	Ensemble machine learning model for classification of handwritten digit recognition 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 2022 DOI:10.1109/MI-STA54861.2022.9837750

[4]	An Efficient CNN Model for Automated Digital Handwritten Digit Classification Journal of Information Systems Engineering and Business Intelligence, 2021 DOI:10.20473/jisebi.7.1.42-55

[5]	Hand Written Digit Recognition using Machine Learning 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), 2020 DOI:10.1109/CSNT48778.2020.9115746

[6]	A Robust Handwritten Digit Recognition System Based on Sliding window with Edit distance 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2020 DOI:10.1109/CONECCT50063.2020.9198385

[7]	Hybrid CNN-GRU model for high efficient handwritten digit recognition Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition, 2019 DOI:10.1145/3357254.3357276

[8]	Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers Journal of Image and Graphics, 2018 DOI:10.18178/joig.6.1.10-20

[9]	Bengali handwritten character recognition using deep convolutional neural network 2017 20th International Conference of Computer and Information Technology (ICCIT), 2017 DOI:10.1109/ICCITECHN.2017.8281853

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