HandWritingRecognition-CNN

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HandWritingRecognition-CNN

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This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.

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display_random.py
figure_1.png
model.png
model_architecture.json
model_weights.h5
predictions.csv
README.md
test.py
test.pyc
train.py

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# Hand Writing Recognition Using Convolutional Neural Networks
![0](http://i.imgur.com/R8JlWvd.png)

## Introduction
This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.

![1](http://i.imgur.com/4o8MTiT.png)
##Usage
The model architecture and weights are saved in the files `model_architecture.json` and `model_weights.h5`. Note that these weights are compatible only with the Tensorflow backed.

To train the model run `train.py`. The file `test.py` generates a file `predictions.csv` which contains the predicted labels to the images in the test set. This file can be used for submission at Kaggle. `display_random.py` displays 25 random images from the test set along with their predicted labels.

![2](http://i.imgur.com/kzBAJEa.png)

## Requirements

* Python 2.7
* [Tensorflow](https://www.tensorflow.org/)
* [Keras](https://keras.io/)
* [h5py](http://www.h5py.org/)
* numpy
* matplotlib
* pandas

## Dataset

* The model is trained on the MNIST dataset downloaded from [Kaggle](https://www.kaggle.com/c/digit-recognizer).

* The file `train.csv` contains pixel intensity values as flattened vectors for 42000 images and their corresponding labels. Similarly, `test.csv` has pixel intensity values for 28000 unlabelled images.

## The Model

HandWritingRecognition-CNN

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