Abstract:

The decipherment and transcription of ancient manuscripts present substantial challenges due to their frequently fragmented, deteriorated, and stylistically heterogeneous nature. This paper introduces an innovative methodology for ancient text recognition through the application of Convolutional Neural Networks (CNNs) implemented on a Raspberry Pi 3B+ for enhanced sensor integration. Our approach leverages advanced deep learning paradigms to preprocess and analyze historical documents, thereby enhancing the precision of text extraction and interpretation. The CNN architecture we propose is trained on a heterogeneous corpus encompassing diverse scripts and languages, thus improving its capacity to generalize across disparate ancient writing systems. We employ techniques such as data augmentation, transfer learning, and customized network architectures to address challenges such as reduced resolution and significant variability in textual appearance. Our empirical results demonstrate that CNN-based methodologies significantly surpass conventional optical character recognition (OCR) approaches, achieving notable advancements in both accuracy and efficiency. This research provides a robust framework for the automated decipherment of ancient texts using the Raspberry Pi 3B+, potentially advancing further historical and philological scholarship.

Keywords- ancient text, deep learning algorithm Convolutional Neural Network(CNN), Raspberry pi3b+