Recognition of MNIST handwritten digits and character set research

Abstract

The goal of the work is the study of influence of descriptors and reduction of their quantity for recognition of MNIST database of handwritten digits.For recognition of the MNIST digits, a set of 12 descriptors was chosen. Statistical analysis of descriptors was performed. Analysis of descriptors gave the reason to assume, that the fifth, sixth and seventh Hu-moments doesn’t contribute into result of digit recognition. Digit recognition with usage of classifier based on on k-means method with n_neighbors = 10 of Scikit-Learn Python system library was done. Best results using 8 descriptors, excluding the fifth, sixth and seventh Hu-moments and eccentricity. Recognition accuracy was 78.58% compared to 78.14%.

Knowledge Extraction in Digit Recognition Using MNIST Dataset

“…Digit recognition systems are being used on a very large scale in today's world because the handwritten digits are not always in the correct size, shape or style. That's why, a lot of problems may occur to find the correct digit in the real world with the help of a machine (Dorosh, N. et al, 2020).…”

“…This research work gives us the motivation to create a model using CNN (Convolution Neural Network) that would be able to receive and identify digits in the form of images. Also, the goal of the program is to learn about CNN, and apply it to handwriting recognition systems (Dorosh, N. et al, 2020). The aim of this research paper is to find the accuracy of CNN to classify handwritten digits using multiple hidden layers.…”

“…CNN is playing a major role in image processing and it is being used in natural small localized areas. CNN is also used in Nano-technologies like classification (Dorosh, N. et al, 2020). There are many different other methods of image processing but CNN is best because of using Keras with the one or multiple layers, which provide multiple layers at a same time to detect fault and classification, that's why high accuracy takes place (Gulli, Antonio et a., 2020).…”