The Recognition of Handwritten Digits Using Neural Network Technology
S. N. Cherny, R. F. Gibadullin
20222022 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)71 citationsDOI
Abstract
In this paper, we compared the quality of processing handwritten digits by a fully connected feedforward neural network and a convolutional neural network. Examples of training and use of both a fully connected neural network and a convolutional neural network are given. The ways of image segmentation and finding the boundaries of handwritten digits for their further analysis are considered. Algorithms affecting the accuracy of learning, the speed of learning are considered. The general principles of the structure and principles of operation of a feed-forward neural network and a convolutional neural network are presented.
Topics & Concepts
NeocognitronConvolutional neural networkComputer scienceTime delay neural networkArtificial neural networkFeedforward neural networkArtificial intelligencePattern recognition (psychology)Probabilistic neural networkSegmentationDeep learningSpeech recognitionImage Processing and 3D ReconstructionAdvanced Computational Techniques in Science and EngineeringIndustrial Engineering and Technologies