A tool for empowering Symbol Detection through Technological Integration in Library Science. A case study on the Voynich manuscript
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Abstract
The Voynich Manuscript, an enduring enigma that has challenged scholars for centuries, remains a formidable hurdle in linguistic decryption and historical cryptography.
This study presents an innovative artificial intelligence-driven methodology, aligned with the overarching goals of the CHANGES project, specifically targeting symbol recognition.
It tackles the intricate task of decoding this ancient manuscript through a refined computational analysis framework. Employing a convolutional neural network trained on an extensive dataset encompassing over 6000 symbols extracted from the manuscript, this approach demonstrates notable strides in both the classification and interpretation of these symbols. This computational tool represents a significant advancement in supporting scholars in symbol recognition and stands as evidence of the CHANGES initiative’s commitment, providing invaluable assistance to researchers striving to unveil the historical and linguistic context embedded within the manuscript’s symbols.