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ocr-devnagri's Introduction

About

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

Requirements

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

Neural-Net Architecture

  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).

Preprocessing

  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.

Data augmentation

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

Results

Using the model an accuracy of 82% was achieved.

ocr-devnagri's People

Contributors

krishnacharya avatar

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