Handwriting Recognition (Digits)
This project uses a neural network to recognize handwritten digits (0–9). It takes images of digits, processes them, and predicts the correct number.
Project Instructions
Import MNIST dataset from TensorFlow/Keras.
Build a CNN model for digit classification.
Train the model (98%+ accuracy possible).
Test with custom handwritten images.
Display predicted digit on screen.
Materials Required
Laptop/PC with Python installed
TensorFlow, Keras, OpenCV
Jupyter Notebook
Circuit Diagram
Code & Programming
Dataset: Uses MNIST, which has 70,000 images of handwritten digits.
Preprocessing: Images are normalized (0–1 range), and labels are one-hot encoded.
Model Architecture:
Input: Flattened 28x28 image
Hidden layers: 128 and 64 neurons with ReLU
Output: 10 neurons with softmax for 10 digits
Training: Model trained for 5 epochs with adam optimizer.
Prediction: Can take custom grayscale images, resize to 28x28, and predict digits.
Video Tutorial
Tips & Tricks
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