Brahmi Script Recognition Using Optimized Convolutional Neural Network with Random Forest Classifier

Optical Character Recognition (OCR) is widely used to digitize printed documents, extract information from forms, automate data entry, and enable text recognition in applications ranging from license plate recognition to handwritten document conversion. Machine learning and deep learning models have recently improved OCR performance, however hyperparameter tuning remains an issue. To solve this, this paper proposes an efficient method for recognizing Brahmi script characters that combines a Convolutional Neural Network (CNN) with a random forest classifier. First, a CNN-based autoencoder extracts features from Brahmi script images, which are then input into the random forest classification model. The hyperparameters of both models are optimized using a genetic algorithm (GA). Extensive experimental results reveal that the proposed approach achieves a significantly better accuracy of 97%, over competitive models.