AI-POWERED RECONSTRUCTION OF HISTORICAL ENGINEERING MANUSCRIPTS USING OPTICAL CHARACTER RECOGNITION
Abstract
Preserving and interpreting historical engineering documents aids in appreciating the nature of scientific reasoning as well as technological advances. Digitization and detailed analysis are highly challenging for many of such documents which are handwritten, eroded and in delicate conditions. This paper presents research on reconstruction of historical engineering documents by employing Optical Character Recognition (OCR) techniques along with Artificial Intelligence (AI) driven machine learning and natural language processing (NLP). With deep learning-based OCR models trained on historical scripts pertaining to specific fields, complex texts, diagrams, and advanced engineering annotations can be extracted, deciphered, and reconstructed accurately. Besides, the engineering text recognition models built in this work utilize contextual understanding that requires the structure of embedded text and documents to improve the accuracy of recognition and creation of engineering document metadata improving retrieval within the archives of documents. Critical analysis of engineering documents of the 18th and 19th centuries demonstrates marked growth in both efficiency and accuracy of transcription as well as speed of processing over conventional OCR techniques. With this research, cultural heritage can be preserved using advanced AI technologies which provide easier access, understanding, and repurposing of ancient information on engineering captured in these documents.