Classification methods for handwritten digit recognition: A survey

Časopis: Vojnotehnicki glasnik

Volume, no: 71 , 1

ISSN: 2217-4753

DOI: 10.5937/vojtehg71-36914

Stranice: 113-135


Introduction/Purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit recognition problem, from linear classifiers to convolutional neural networks. Results: Handwritten digit recognition classification accuracy tested on the MNIST dataset while using training and testing sets is now higher than 99.5% and the most successful method is a convolutional neural network. Conclusions: Handwritten digit recognition is a problem with numerous real-life applications. Accurate recognition of various handwriting styles, specifically digits is a task studied for decades and this paper summarizes the achieved results. The best results have been achieved with convolutional neural networks while the worst methods are linear classifiers. The convolutional neural networks give better results if the dataset is expended with data augmentation.
Ključne reči: handwritten digit recognition, image classification, supportvector machine, deep neural networks, convolutional neural networks, hyperparameter optimization, swarm intelligence, MNIST
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