Image-Recognition-CNN-Handwritten-Digit-Classification

Handwritten digit classification using data augmentation

Overview A Deep CNN implementation with Keras to recognize Handwritten Digits

Libraries

Numpy Matplotlib Seaborn Skimage Sklearn Keras

Parameters:

Training “easy” blind test data set

1. For “easy” blind test data set all the parameters(ex epoch, Optimizer, batch_size) are listed in the beggining of the /Handwritten-Character-Recognition/train.py file.

2. Please specifing the paths for the dataset in test.py using the variables Xload and Yload.

3. Please place the files of the dataset, label set and the trained_model. hdf5 in the Handwritten-Character-Recognition folder.

4. The best model will be saved in the ./Handwritten-Character-Recognition/trained_model.hdf5 .

Testing “easy” blind test data set

1. For “easy” blind test data set all the parameters are listed in the beggining of the Handwritten-Character-Recognition/test.py file.

2. For specifying the paths for the files of the dataset, please use the test_data and test_labels variable.

3. Please place the files of the dataset, label set and the trained_model. hdf5 in the Handwritten-Character-Recognition folder.

How to run test.py

1. Open the file and change the path to test_data and test_labels:
   
2. cd Handwritten-Character-Recognition
   -------python test.py