Implementation of Recognition Model for Ancient Kannada Epigraphs

ABSTRACTThe decipherment and transcription of ancient manuscripts present substantial challenges due to their frequentlyfragmented, deteriorated, and stylistically heterogeneous nature. This paper introduces an innovative methodology forancient text recognition through the application of Convolutional Neural Networks (CNNs) implemented on a RaspberryPi 3B+ for enhanced sensor integration. The approach leverages advanced deep learning paradigms to preprocess andanalyze historical documents, thereby enhancing the precision of text extraction and interpretation. The CNN architectureproposed is trained on a heterogeneous corpus encompassing diverse scripts and languages, thus improving its capacity togeneralize across disparate ancient writing systems. Techniques such as data augmentation, transfer learning, andcustomized network architectures are employed to address challenges such as reduced resolution and significantvariability in textual appearance. Empirical results demonstrate that CNN-based methodologies significantly surpassconventional optical character recognition (OCR) approaches, achieving notable advancements in both accuracy andefficiency. This research provides a robust framework for the automated decipherment of ancient texts using theRaspberry Pi 3B+, potentially advancing further historical and philological scholarship.