Image Recognition Using Bayesian Networks
DOI:
https://doi.org/10.31649/1997-9266-2024-175-4-115-121Keywords:
recognition, Bayesian network, learning, testing, MNIST, probability, neural network, datasetAbstract
Neural networks and Bayesian networks are powerful machine learning methods used to address a wide range of tasks. Neural networks are biologically inspired systems consisting of interconnected neurons that process information and transmit the results to other neurons. Bayesian networks, also known as belief networks or causal probabilistic networks, are a type of probabilistic graphical model. They are used to represent dependencies between variables and calculate the probabilities of different events. Bayesian networks enable to compute the probability of a certain event, taking into account other known events. They utilize Bayes’ theorem to update the probabilities of variables in the network. Their intuitiveness, flexibility, efficiency, and integrative nature make Bayesian networks relevant in many application areas. The aim of this work is to develop and test a Bayesian neural network for recognizing handwritten digits. In this study, a multilayer perceptron Bayesian network was developed and tested for the classification of handwritten digits. The MNIST dataset was used for model training, which contains 70,000 images of handwritten digits with labels. This dataset is widely used for testing image recognition algorithms. To evaluate the network’s effectiveness, a test subset of data containing 10,000 images of handwritten digits was used. The developed model demonstrated the accuracy of 93.92 %, which is a better result than other machine learning methods for recognizing handwritten digits. The given model could be useful for developing automatic text recognition systems, such as postal sorting machines and check scanners. The research demonstrates that the Bayesian network is a promising method for classifying handwritten digits, as confirmed by the study. Therefore, it can be concluded that Bayesian networks are not flawless. Their accuracy depends on the quality of data and the correctness of the model. However, if used correctly, they can be a powerful tool for detecting patterns and making decisions.
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