August 11, 2025

Handwritten Digit Pattern Recognition by Hybrid of Convolutional Neural Network (CNN) and Bosting Classifiers

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Authors

ABArunima BanerjeeNational Centre for Radio Astrophysics NSNitu SahaUniversity of Engineering & Management WAWashim Akram

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Implication

This observation demonstrates 99.51% accuracy using CNN and boosting classifiers in handwritten digit recognition, indicating improvements in machine learning techniques.

Key Points

The integrated model achieved 99.51% accuracy using CNN with boosting classifiers for handwritten digits.
Testing with the MNIST and EMNIST datasets revealed significant performance enhancements.
Applying convolutional neural networks improves feature extraction in digit recognition models.
Findings suggest that hybrid techniques can greatly enhance classification accuracy in pattern recognition tasks.

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Banerjee et al. (2025) studied this question.

synapsesocial.com/papers/68a35ef30a429f79733281d6 https://doi.org/10.38124/ijisrt/25jul782

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Implication

This observation demonstrates 99.51% accuracy using CNN and boosting classifiers in handwritten digit recognition, indicating improvements in machine learning techniques.

Handwritten Digit Pattern Recognition by Hybrid of Convolutional Neural Network (CNN) and Bosting Classifiers

Discussion

Member takes

Implication

Key Points

Cite This Study

Handwritten Digit Pattern Recognition by Hybrid of Convolutional Neural Network (CNN) and Bosting Classifiers

Discussion

Member takes

Implication

Key Points

Cite This Study

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