maneprajakta / Digit_Recognition_Web_App

A Hand Written Digit Recognition app trained on the MNIST dataset of Keras using the CNN model. skills used are Tensorflow, HTML,CSS,javascript.

Digit_Recognition_Web_App

link : https://maneprajakta.github.io/Digit_Recognition_Web_App/

Structure of App

keras - > Tensorflow.js ->(html + css + javascript)->github pages

Hello World of Object Recognition!

Aim:

To make a convolution neural network to recognise handwritten digits by training the model on MNIST dataset available in keras.

MNIST DATASET:

The training dataset contain 60000 images and testing contain 10000 images .Each image is 28x28 pixel and grey scale.

CNN MODEL OVERVIEW:

⚈ It is a 17 layer model with Conv2D,MaxPooling2D,BatchNormalization,Dense,Flatten and Dropout layer combination.
⚈ Input layer has 32 neuron and output layer has 10 neurons as 10 different clases exsist.
⚈ 30 epochs are used.
⚈ Categorical_loss is loss function and adam is used for optimization.
⚈ Model gives 99.15% accuracy.

For Deployment:

Save model using tensorflowjs converters as json file and weight as .h5 file.Use Tensorflow.js to load model and predict in javascript file