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Handwritten Digital Image Recognition based on Fusion of Multiple Machine Vision Algorithms

Published: 26 March 2024 Publication History

Abstract

Handwritten digit recognition technology is increasingly used in a wide range of fields, but the recognition rate still has room for further improvement. Currently, there are a large number of studies focusing on the use of activation functions to improve the handwritten digit recognition rate, compared with ReLU, Sigmod and other activation functions, this paper chooses to take the Mish activation function to optimise the handwritten digit recognition algorithm, using Pytorch machine learning framework, designed a deep convolutional neural network model based on the MNIST dataset to carry out digit recognition, and to improve the accuracy of model recognition through the learning of image features for digit classification to improve the model recognition accuracy. The experimental results show that the Mish activation function can increase the accuracy of handwritten digit recognition to 99%.
Keywords: Handwritten digit recognition, Deep convolutional neural network, Mish activation function, MNIST Dataset, Pytorch

References

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ICITEE '23: Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering
November 2023
764 pages
ISBN:9798400708299
DOI:10.1145/3640115
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2024

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  • China Innovation and Entrepreneurship Training Program for College Students

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ICITEE 2023

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