Vol.12, No.3, August 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Using a Convolutional Neural Network for Machine Written Character Recognition


Ladislav Karrach, Elena Pivarčiová


© 2023 Elena Pivarčiová, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 12, Issue 3, Pages 1252-1259, ISSN 2217-8309, DOI: 10.18421/TEM123-03, August 2023.


Received: 30 March 2023.

Revised:   11 June 2023.
Accepted: 19 July 2023.
Published: 28 August 2023.




Convolutional neural networks are special types of artificial neural networks that can solve various tasks in computer vision, such as image classification, object detection, and general recognition. The paper presents the basic building blocks of convolutional neural networks and their architecture, and compares their recognition accuracy with other character recognition techniques using the example of character recognition from vehicle registration plates. The purpose of the experiments was to determine the optimal configuration of the convolutional neural network and the influence of the size and design method of the training set on the recognition rate. The study shows that although convolutional neural networks have recently gained attention, traditional recognition methods are still relevant, and the choice of the right classifier and its configuration depends on the type of recognition task.


Keywords –Convolutional neural network, optical character recognition, feature vector, feature extraction, classification.



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