Classification methods for handwritten digit recognition: A survey - Tuba, Ira M.; Tuba, Una M.; Veinović, Mladen Đ. (Vojnotehnički glasnik / Military Technical Courier, 2023)
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 develop- ment of different classifiers applied to the handwritten digit recognition prob- lem, 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 summa- rizes the achieved results. The best results have been achieved with convolutional neural networks while the worst methods are linear classifiers. The con- volutional neural networks give better results if the dataset is expended with data augmentation.