Handwritten digit recognition uses convolutional neural networks to recognize handwritten digits from images. The MNIST dataset, containing 60,000 training images and 10,000 test images of handwritten digits, is used to train models. Convolutional neural network architectures for this task typically involve convolutional layers to extract features, followed by flatten and dense layers to classify digits. When trained on the MNIST dataset, convolutional neural networks can accurately recognize handwritten digits in test images.

1. Handwritten digit
recognition
Bhushan S. Tandale
Eckovation

2. MAIN GOAL & APPLICATIONS
• Handwritten Digit Recognition is used to recognize the Digits
which are written by
hand.
• A handwritten digit recognition system is used to visualize
artificial neural networks.
• It is already widely used in the automatic processing of bank
cheques, postal addresses, in mobile phones etc

3. INTRODUCTION
• Scientists believe that the most intelligent device is the Human
Brain.
• There is no computer which can beat the level of efficiency of
human brain. These Inefficiencies
of the computer has lead to evolution of “Artificial Neural Network”.
• They differ from conventional systems in the sense that rather
than being programmed these
system learn to recognize pattern.

4. NEURAL NETWORK
Artificial neural networks, usually called neural networks (NNs), are
interconnected systems composed of many simple processing elements
(neurons) operating in parallel whose function is determined by
1)Network Structure
2)Connection Strengths
3)The Processing performed at Computing elements or nodes.

5. A NEURAL CELL OF THE HUMAN

6. THE MNIST DATABASE
• Training of the network is done by a dataset named
MNIST dataset.
• MNIST dataset has a training set of 60,000 examples,
and a test set of 10,000 examples.
• All the images in the dataset are of 28x28 pixels.

7. Why Convolutions?
Convolution is a simple mathematical operation between two matrices in
which one is multiplied to the other element wise and sum of all these
multiplications is calculated.
 Convolutions are performed for various reasons-
• Convolutions provide better feature extraction
• They save a lot of computation compared to ANNs.
• Less number of parameters are created than those in pure fully connected
layers.
• Due to less number of required parameters, lesser fully connected layers
are needed.

8. ARCHITECTURE OF CONVOLUTION NEURAL
NETWORK
• Two layers will be convolution layers the first with 64 channels, a 3 x 3
kernel and Rectifier Linear Unit (ReLu) function which will feed 64 images
into the second layer
• While the second layer will have 32 channels, a 3 x 3 kernel and Rectifier
Linear Unit (ReLu) function and feed 32 images into the third layer.
• The third layer is the flatten layer to transform the dimentionality of the
image to a 1-Dimension array to connect with the last layer which
contains 10 neurons and the activation function softmax.

9. ARCHITECTURE OF CONVOLUTION NEURAL
NETWORK

10. TRAINING MODEL
Train the model on the training data set ( X_train and y_train). I will iterate
3 times over the entire data set to train on, with a number of 32 samples per
gradient update for training. Then store this trained model into the
variable hist.

11. TESTING MODEL

12. CONCLUSION
• The handwritten digit recognition using convolutional neural
network has proved to be of a fairly good efficiency.
• It works better than any other algorithm, including artificial neural
networks.