Handwritten Digit Recognition using Machine and Deep Learning Algorithms
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This paper has performed handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models.Abstract:
The reliance of humans over machines has never been so high such that from object classification in photographs to adding sound to silent movies everything can be performed with the help of deep learning and machine learning algorithms. Likewise, Handwritten text recognition is one of the significant areas of research and development with a streaming number of possibilities that could be attained. Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices [1]. Apparently, in this paper, we have performed handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main objective is to compare the accuracy of the models stated above along with their execution time to get the best possible model for digit recognition.read more
Citations
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Proceedings ArticleDOI
A Robust Model for Handwritten Digit Recognition using Machine and Deep Learning Technique
TL;DR: In this paper, a CNN based on the kara-model was used to classify handwritten digit images with RMSprop optimizer for optimizing the model, which achieved 99.06% of training accuracy and 98.80% of testing accuracy with epoch 10.
Journal ArticleDOI
Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition
Ebrahim Al-wajih,Rozaida Ghazali +1 more
TL;DR: In this article , a new model based on center-symmetric local binary patterns (CS-LBPs) is proposed, which addresses the issues of LBCNNs.
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Adaptive Graph Representation for Clustering
TL;DR: Wang et al. as discussed by the authors proposed an adaptive graph construction and low-rank representation of weighted noise (ACLWN) model, which is composed of an adaptive representation graph construction model named ARG and an adaptive weighted sparse representation graph learning model named AWSG.
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A Novel Technique for Handwritten Digit Recognition Using Deep Learning
TL;DR: In this paper , a deep learning-based technique, namely, EfficientDet-D4, was proposed to detect and categorize the numerals into their respective classes from zero to nine, achieving an average accuracy of 99.83%.
References
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Handwritten Digit Recognition Using Machine Learning Algorithms
TL;DR: In this paper, the authors presented an approach to off-line handwritten digit recognition based on different machine learning techniques (i.e., Multilayer Perceptron, Support Vector Machine, Naive Bayes, Bayes Net, Random Forest, J48, and Random Tree) using WEKA.