Kalken M.2024-12-192024-12-192022https://repository.sdu.edu.kz/handle/123456789/1588With the transformation to digital information exchange, many papers, including invoices, taxes, notes and surveys, historical data, and test results, still require handwriting. In this context, handwritten digit recognition is required, which is a computer-automated method for decoding records. To achieve this purpose, machine learning mostly employs neural networks, with the convolution neural network being the most common. The primary objective of this dissertation is to develop an automatic technique for detecting handwritten digits by determining the optimal hyperparameter of a convolutional neural network and analyzing the impact of various hyperparameters on network performance. To do this, several values of each hyperparameter were chosen within a specified range using the MNIST database for training and testing. As a result, a large amount of data was collected in which a very high influence of hyperparameter selection on the performance of the neural network was determined, and a 99.2% accurate pure CNN design was achieved.endigital information exchange, invoices, taxes, historical data, handwriting, CNNOther