cruil / HandWritingRecognition-CNN

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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Hand Writing Recognition Using Convolutional Neural Networks

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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 ##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.

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Requirements

Dataset

  • The model is trained on the MNIST dataset downloaded from Kaggle.

  • 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

About

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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