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

Home Page: https://maneprajakta.github.io/Digit_Recognition_Web_App/

License: MIT License

CSS 8.16% HTML 9.67% JavaScript 19.88% Jupyter Notebook 62.29%
machile-learning digit-recognition mnist-dataset convolutional-neural-networks webapp

digit_recognition_web_app's Introduction

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

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