A NOVEL CUTTING EDGE NATURE INSPIRED OPTIMIZATION OF BRAHMI CHARACTERS RECOGNITION

Abstract

Feature extraction techniques and classification tools have been used in Optical Character Recognition for many years. Machine learning models and Convolutional Neural Networks (CNN) have been proven more effective in handwritten character recognition. Optimizing the hyperparameters of machine learning models is still a challenging task. Hence, this work accomplishes Brahmi character recognition by using a convolutional neural network and random forest classifier where hyperparameter tuning of the machine learning model has been improved using a genetic algorithm. The characteristics from the images of Brahmi letters are extracted using a CNN-based autoencoder fed to the random forest classifier. The suggested Algorithm's performance is compared to other existing algorithms using performance parameters. It achieves an accuracy of 97% which is much better than the 92% or 94% accuracy achieved by CNN-only or CNN-based random forest.