An Optical Character Recognition(OCR) for the devanagri script using Convolutional Neural Networks(CNNs).

The image may contain more than one character. Therefore this is a case of multi-label multi-class classification.

1. The Output layer of net contains 128 nodes corresponding to each devanagri character.
2. The input layer is a Convolutional layer taking images of dimension 64*64
3. The hidden layer consists of Convlational layers and Max-Pooling layers with a ReLu activation function
4. A sigmoid activation function is used in the last layer, with a threshold value of 0.5 per character. Softmax isn't suited for multi-label classification(read).

1. A Gaussian blur filter is used for noise removal.
2. Binarization - Images are converted to grayscale then OTSU's method is used for grayscale to binary image conversion.
3. The images are resized to 64*64 dimension.

The Keras Image Generator is used to augment training data by rotating and shifting transformations on the fly.

Using the model an accuracy of 82% was achieved.