Recognizes hand written digits in real time using CNN.

1. Digit-Recognizer
… a wonderful application of CNN
Made by: Awismrit Parida
(Eckovation)
Course: Machine Learning in Python

2. Abstract
 Recognizes real time hand written digits using CNN (Convolution
neural networks)
 Repository Link: https://github.com/AWIS99/Digit-Recognizer

3. Requirements
 Python v3.7
 TensorFlow v2.1.0
 OpenCV v3.4.7
 Keras
 NumPy
*one can install anaconda which solves most of the dependencies

4. Steps of Development
 Make sure that your development environment is ready and fulfils all
the previously-mentioned requirements.
 Download the dataset Fashion MNIST which is built into the
TensorFlow and split it into training data, training labels testing data
and testing labels.

5.  Normalise the data for faster processing.

6.  Define the model (CNN) according to the given architecture below in
this report, followed by compilation, training and evaluation.

7.  Save the generated model for later use.
 Next is OpenCV part. Import all required libraries. Load the saved
model and execute the OpenCV code ( get the code from repository ).
It’s done.

8. Usage
 Download all the files in the repository.
 Copy the file path of 'digit.h5' which is the trained model with a test
accuracy of over 99%.
 Open the 'OpenCV_code.py' file in any text editor and replace the
path inside the load_model function and save it.
 Now run the file in your configured terminal/cmd.
 Take any blue coloured pointer and start drawing in front of webcam.
The detected digits will be shown in the bottom left corner.
 Enjoy!!
 You can also do the same in Jupiter notebook with the ipynb files.

9. Architecture
 Conv2D-(64) --> MaxPool-(2/2) --> Conv2D-(64) -
-> MaxPool-(2/2) --> Dense-(128,relu) --> Dense-
(10,softmax) --> Classification
 Train Accuracy ~ 99.74
 Test Accuracy ~ 99.11

10. Conclusion
We have successfully developed a real time
digit classifier using Convolution Neural
Networks.