Handwritten Digits Classification using TensorFlow

Introduction

• Target audience: Extreme beginners in computer vision and deep learning.
• Demo: Recognizing handwritten digits from 0 to 9 using a deep learning architecture based on Microsoft Paint.

Understanding Images

• An image is represented as a matrix of integers (e.g., 800x600 pixels).
• Pixel values range from 0 (black) to 255 (white), with values in between representing shades of gray.
• Color images have three channels (Red, Green, Blue).

Handwritten Digit Images

• MNIST dataset: Contains 60,000 images of handwritten digits (28x28 pixels).
• Digits can be written in different styles by different people.

Deep Learning Architecture Overview

1. Convolution Layer
   • Acts as a feature extractor by using filters (kernels).
   • Example: A 5x5 filter extracts features based on pixel values.
2. Pooling Layer
   • Reduces the size of feature maps (e.g., max pooling).
   • Helps retain important features while reducing computation.
3. Dense Layer
   • Fully connected layer that sums up all extracted features.
   • The final dense layer corresponds to the number of classes (10 for digits 0-9).
4. Decision Layer
   • Classification layer that predicts the digit.

Data Preparation

• Normalization: Scaling pixel values between 0 and 1 for better convergence.
• Train/Test Split: Important to separate training and testing datasets to evaluate model performance.
• MNIST dataset loaded via TensorFlow.

Implementation Steps

1. Environment Setup
   • Use Anaconda and Jupyter Notebook for coding.
   • Install necessary libraries: TensorFlow, OpenCV, Matplotlib, NumPy.
2. Loading the Dataset
   • Use TensorFlow's keras API to load the MNIST dataset.
   • Split dataset into training and testing data.
3. Model Creation
   • Utilize the Sequential model from TensorFlow.
   • Add convolution, pooling, and dense layers accordingly.
   • Use activation functions (e.g., ReLU for hidden layers, softmax for the output layer).
4. Model Compilation
   • Use Adam optimizer and categorical cross-entropy loss for multi-class classification.
5. Model Training
   • Train the model using model.fit() on training data.
   • Monitor accuracy during training and validation.
6. Model Evaluation
   • Test the model on the test dataset for accuracy evaluation.

Prediction and Visualization

• Predict handwritten digits using the trained model.
• Process custom images: Resize, convert to grayscale, normalize, and reshape for model input.
• Display predictions for user-drawn digits.

Demo

• A video demonstration was provided, showing real-time prediction of handwritten digits using a webcam.

Conclusion

• The lecture covered the basics of implementing a handwritten digit classification project using TensorFlow.
• Encouraged viewers to explore more on computer vision and deep learning.