Deep Learning Based Optical Character Recognition Model for Old Turkic

This study presents the development and evaluation of a deep learning-based optical character recognition (OCR) model specifically designed for recognizing Old Turkic script. Utilizing a convolutional neural network (CNN), the project aimed to achieve high classification accuracy across a dataset comprising 38 distinct Old Turkic characters. To enhance the model’s robustness and generalization capabilities, sophisticated data augmentation techniques were employed, generating 760 augmented images from the original 38 characters. The model was rigorously trained and validated, achieving an overall ac- curacy of 96.34%. Evaluation metrics such as precision, recall, and F1-scores were systematically analyzed, showing superior performance in most classes while identifying areas for further optimization. The results underscore the effectiveness of CNN architectures in specialized OCR tasks, demonstrating their potential in preserving and digitizing historical scripts. This study not only advances the field of document analysis and OCR but also contributes to the digital preservation and accessibility of ancient scripts.