Abstract
Converting real written data and information into digital form for storage in a digital format necessitates the use of expensive manual labor. The paper aims to debate a response to some of the issues since the spectrum has been limited to just handwritten digits (0–9). The ‘MNIST DATASET,’ which includes training and test datasets for handwritten digits (0–9) with a resolution of (28 × 28), or 784 pixels, was used. The MNIST data set contains 60,000 training data and 10,000 test data.
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Praveen, P., Abhishek, N., Reddy, K.J., Raj, C.T., Reddy, A.Y. (2022). A Predictive Model of Handwritten Digits Recognition Using Expert Systems. In: Ranganathan, G., Fernando, X., Shi, F., El Allioui, Y. (eds) Soft Computing for Security Applications . Advances in Intelligent Systems and Computing, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-5301-8_43
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