MNIST Digit Classification Deep Learning Approach for Efficient Handwritten Digit Recognition

Mr. P. Chaitanya; D Deepika; G Jyothi Sri; K Aditya Ram; G Jeevan Sai Kiran

doi:10.64643/ijirtv12i10-194957-459

April 6, 2026International Journal of Innovative Research in TechnologyOpen Access

MNIST Digit Classification Deep Learning Approach for Efficient Handwritten Digit Recognition

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Key Points

The objective is to assess the performance of deep learning techniques in classifying handwritten digits from the MNIST dataset.
Utilized deep learning algorithms for classification tasks.
Analyzed the MNIST dataset comprising thousands of handwritten digits.
Evaluated model performance based on accuracy and efficiency metrics.
Achieved high accuracy rates in digit recognition tasks.
Demonstrated improved processing speeds over traditional methods.

Implication

Study evaluates a deep learning method for handwritten digit recognition, indicating potential for efficiency.

Abstract

Explore the article titled MNIST Digit Classification Deep Learning Approach for Efficient Handwritten Digit Recognition from IJIRT Volume 12, Issue 10. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

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Cite This Study

Chaitanya et al. (2026) studied this question.

synapsesocial.com/papers/69d8946e6c1944d70ce05532 https://doi.org/10.64643/ijirtv12i10-194957-459

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MNIST Digit Classification Deep Learning Approach for Efficient Handwritten Digit Recognition

Key Points

Implication

Abstract

Discussion

Cite This Study

MNIST Digit Classification Deep Learning Approach for Efficient Handwritten Digit Recognition

Key Points

Implication

Abstract

Discussion

Cite This Study

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