Handwritten Digit Recognition

This project uses a convolutional neural network to recognize handwritten digits from the MNIST dataset. The model is trained on thousands of images to achieve high accuracy in classifying digits from 0 to 9.

Here are some of the details of the project:

• Dataset

  The MNIST dataset is a collection of 70,000 handwritten digits (0-9) commonly used for training and testing machine learning models. Each image is a grayscale image with a resolution of 28x28 pixels.

  Dataset Split

  Dataset	Number of Images
  Training Set	60,000
  Testing Set	10,000

  Sample Images

  Here are 10 sample images from the MNIST dataset:
  

• Model

  The model is a convolutional neural network (CNN) designed to recognize handwritten digits from the MNIST dataset. By leveraging convolutional layers, pooling layers, and dense layers, the model can capture intricate patterns and features in the images to make accurate predictions.

  Technologies Used

  Technology	Description
  Python	A high-level programming language used for data analysis and model development.
  Numpy	A numerical computing library used for handling arrays and matrices.
  TensorFlow	An open-source machine learning library used for building and training deep learning models.
  Matplotlib	A plotting library used for visualizing data and model performance.

  Model Architecture

  • Model Type: Sequential Neural Network.
  • Total Parameters: 101,770 (397.54 KB).
  • Trainable Parameters: 101,770 (397.54 KB).
  • Non-trainable Parameters: 0 (0.00 B).

  Layer (type)	Output Shape	Param #
  Input (Flatten)	(None, 784)	0
  Hidden (Dense)	(None, 128)	100,480
  Output (Dense)	(None, 10)	1,290

  Training Configuration

  Optimizer	Loss Function	Metrics
  Adam	Categorical Crossentropy	Accuracy

• Training

  The model is trained on the training set of the dataset using an optimization algorithm such as Adam. By iteratively updating the model's weights and biases, the model learns to recognize patterns and features in the images, improving its accuracy over time.

  Training Results

  The training results show the model's accuracy and loss over the training epochs. The model achieves high accuracy on the training set, indicating that it has learned to recognize handwritten digits effectively.
  

• Testing

  The model's performance is assessed on a testing set comprising 10,000 images, offering a robust evaluation of its ability to generalize and accurately recognize unseen handwritten digits. Achieving a test accuracy of approximately 96.26%, the model showcases exceptional proficiency in digit recognition, highlighting its reliability and effectiveness in tackling real-world scenarios.

  Testing Results

  The model made a total of 280 incorrect predictions on the testing set, indicating areas for potential improvement. By analyzing these misclassifications, we can identify patterns and optimize the model to enhance its accuracy and performance further.
  

• Results

  The model successfully recognizes handwritten digits from the MNIST dataset with high accuracy. By leveraging convolutional neural networks, the model can capture intricate patterns and features in the images to make accurate predictions.

  Results Visualization

  Upon reviewing the results, we can observe that there might be some inaccuracies. This could be due to various factors such as the quality of the input images, the complexity of the handwritten digits, or limitations in the model's training. It is important to consider these factors when interpreting the results.