The application of convolutional neural network in the context of Tamil handwritten character recognition

Abstract

This research paper presents an in-depth investigation into the application of Convolutional Neural Networks (CNNs) for Tamil handwritten character recognition. We explore existing research, methodologies, and cutting-edge techniques, showcasing CNNs' effectiveness in achieving a remarkable 95% accuracy. Our dataset comprises 247 Tamil characters and 18 North Indian characters, accommodating diverse writing styles. We tailor CNN architectures for Tamil characters, implement advanced preprocessing, data augmentation, and training methods to enhance model performance. Our paper tackles challenges posed by accessible datasets, offering remedies for data scarcity, class imbalance, and writing style variations. Our distinct contribution lies in achieving 95% accuracy across 247 Tamil characters and 18 North Indian characters, demonstrating CNNs' potential for document processing, language preservation, and automation in Tamil-speaking regions. This work advances the field by introducing novel techniques, a comprehensive dataset, and strategic insights, serving as a significant step forward in Tamil character recognition.