An Optimize Revolutionary Novel AI-Driven Historical Inscription of Low Script and Ancient Manuscript Using OCR and NLP

Analyzing ancient manuscripts and unknown scripts presents a very difficult problem, because of document deterioration and linguistic differences. Recent progress in Artificial Intelligence (AI) in the fields of Optical Character Recognition (OCR) and Natural Language Processing (NLP) is making it possible to automate the analysis and interpretation of historical texts. This paper has contributed an artificial intelligence-based framework deep learning embedding OCR and NLP to reconstruct ancient scripts. The proposed work quality and accuracy prediction of the inscription, the system uses deep learning-based OCR models trained with historical datasets that are used for text extraction from degraded manuscripts. The methodology of Deept Learning Techniques Convolutional Neural networks (CNNs), and Recurrent Neural Network (RNN). The transformer architectures, improve recognition despite being made of detached inscriptions. After OCR, NLP procedures, like Named Entity Recognition (NER), Part-of-speech (POS) tagging, and sequence-to-sequence translation, are used to assign contextual text interpretation. The combination of transfer learning and attention mechanisms enhances the ability to adapt to various scripts and languages. By using supervised and unsupervised learning techniques, the system results in improved accuracy. A highly specialized dataset of digitized ancient scripts can be used to train, and a linguistic knowledge base complements the understanding of the context. Bayesian inference and Hidden Markov Models (HMM) are used for rewriting missing texts. Additionally, cloud-based resources facilitate large-scale manuscript processing, ensuring scalability. Such a web interface enables researchers to import and process ancient texts in real time. Preliminary evaluations show great enhancement in text recognition and understanding with respect to conventional manual approaches. The present study enriches the fields of epigraphy, historical linguistics, and manuscript studies with the ability to automate the script decipherment and the storage of historical information. Further improvements will lie in the detail of the language models and the incorporation of reinforcement learning to enhance the text reconstruction and with this facilitate the exploration of ancient civilizations and cultural development.