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This is a Quantitative Study study.

September 12, 2025INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTOpen Access

Handwritten English Digit Recognition

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Authors

HPHameem Khan PPondicherry University MSMohammad Bagher Naghibi SistaniSaveetha University RAR S Abhijna

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Implication

This project develops a CNN model improving digit recognition accuracy on the MNIST dataset, highlighting machine learning's utility in computer vision.

Key Points

The model achieves high accuracy in recognizing handwritten digits through extensive hyperparameter tuning and experimentation.
A convolutional neural network (CNN) architecture is employed, utilizing convolutional, pooling, and connected layers for effective classification.
The approach focuses on feature extraction, demonstrating CNNs' potential in learning spatial hierarchies from input images.
This system can enhance applications in postal automation, bank check processing, and digitization of handwritten forms.

Cite This Study

P et al. (2025) studied this question.

synapsesocial.com/papers/68d44c3d31b076d99fa55745 https://doi.org/10.55041/ijsrem52522

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Implication

This project develops a CNN model improving digit recognition accuracy on the MNIST dataset, highlighting machine learning's utility in computer vision.

Handwritten English Digit Recognition

Discussion

Member takes

Implication

Key Points

Cite This Study

Handwritten English Digit Recognition

Discussion

Member takes

Implication

Key Points

Cite This Study

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