Abstract

The preservation and interpretation of ancient scripts are essential for uncovering the rich cultural heritage and historical knowledge of early civilizations. Tamil, one of the world’s oldest languages, contains a vast repository of information preserved in inscriptions, manuscripts, and other historical artifacts. This research proposes a novel methodology for the recognition and deciphering of ancient Tamil words from historical documents and artifacts using image processing techniques and deep learning (DL) algorithms. The proposed framework consists of three main stages: pre-processing, feature extraction, and DL-based recognition. A region-based convolutional neural network (RCNN) architecture is employed to automatically learn and identify the intricate patterns and structural elements of ancient Tamil characters. To enhance feature extraction for irregularly shaped characters, this work introduces Adaptive region of interest (ROI) pooling, which dynamically adjusts to variations in stroke patterns and inscription styles, thereby improving recognition accuracy. The RCNN is trained on a large annotated dataset of ancient Tamil word images, with labels verified by domain experts to ensure data accuracy and reliability. Extensive experiments were conducted on a diverse dataset comprising stone inscriptions, palm leaf manuscripts, and clay tablets. The proposed approach achieved a high recognition accuracy of 98.6%, demonstrating robust performance even under challenging conditions such as stylistic variations, surface degradation, and image noise. This method significantly contributes to the preservation of cultural heritage by enabling the digitization and accessibility of historical texts and inscriptions. By safeguarding valuable linguistic and cultural knowledge, the proposed system ensures its availability for future scholarly research and education.

Keywords

Ancient Tamil, Character recognition, Segmentation, RCNN, Deep learning, Computer vision.

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