Abstract
A remarkable and significant problem is Digit Recognition. The digit recognizer problem refers to the task of correctly identifying handwritten digits from images. The problem of handwritten digit recognition must be understood in the context of a variety of challenges since the manually written digits do not have uniform sizes, thicknesses, positions, or directions. The individuality and variety of compositional approaches of different people also have an impact on the example and presence of the digits. This paper looks at how machine learning (ML) methods can be used to solve the “digit recognizer problem” in an effective way and compares the performance of several machine learning algorithms, including support vector machine (SVM), convolutional neural network (CNN), multilayer perceptron (MLP), random forest (RF), and logistic regression (LR), on the MNIST dataset of handwritten digits. The results show that neural networks, specifically CNN, achieve the highest accuracy for the digit recognizer problem. Furthermore, this paper discusses the advantages and limitations of each approach and provides insights on how to improve their performance.
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Shakoor, U., Mim, S.S., Logofatu, D. (2023). Use of Machine Learning Algorithms to Analyze the Digit Recognizer Problem in an Effective Manner. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_40
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