Handwritten Digit Recognition
Project Title : Handwritten Digit Recognition
Objective:
To develop an AI model that can accurately recognize and classify handwritten digits (0–9) from images, similar to how humans read digits.
Technologies Used:
Programming Language: Python
Libraries/Frameworks: NumPy, Pandas, Matplotlib, TensorFlow / Keras or PyTorch
Dataset: MNIST (Modified National Institute of Standards and Technology)
Approach:
Data Collection:
Use the MNIST dataset, which contains 60,000 training and 10,000 test images of handwritten digits (28x28 pixels, grayscale).
Data Preprocessing:
Normalize pixel values (0 to 1)
Reshape data for input into neural networks
One-hot encode the output labels (for classification)
Model Building:
Build a Convolutional Neural Network (CNN)
Layers: Convolution → ReLU → Pooling → Fully Connected → Output
Optionally experiment with simpler models like logistic regression or MLP
Training & Evaluation:
Train the model on the training set
Evaluate performance using accuracy, confusion matrix, and loss graphs
Testing & Prediction:
Test on new images
Display predictions with visual outputs (optional using matplotlib or GUI)
Outcome:
An AI system capable of recognizing handwritten digits with high accuracy (>98%), showcasing the power of deep learning in image recognition tasks.
₹ 699 /-
Project includes:
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Customization
Fully
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Security
High
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Performance
Fast
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Future Updates
Free
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Total Buyers
500+
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Support
Lifetime
Secure Payment:
