AI-Powered Manuscript Heritage: Deep Learning, OCR, and Digital Preservation Across Civilizations
Language: English
Published by Shark Nail, 2026
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
AbeBooks seller since October 27, 2023
Condition: New
£ 27.82
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Add to basketSeller Inventory # I-9781962116633
- Title
- AI-Powered Manuscript Heritage: Deep Learning, OCR, and Digital Preservation Across Civilizations
- Author
- Thimon
- Publisher
- Shark Nail
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1962116638
- ISBN 13
- 9781962116633
AI-Powered Manuscript Heritage: Deep Learning, OCR, and Digital Preservation Across Civilizations examines the intersection of artificial intelligence, historical manuscripts, optical character recognition, deep learning, and digital cultural heritage. Manuscripts represent some of humanity's most enduring records of intellectual, literary, scientific, religious, administrative, and artistic activity. Their preservation and interpretation increasingly depend on computational methods capable of processing complex, degraded, multilingual, and historically diverse documents. This book explores how modern artificial intelligence and document analysis technologies can contribute to the digitization, recognition, interpretation, and long-term preservation of manuscript heritage.
The book begins by establishing the cultural and historical importance of manuscripts as vessels through which knowledge has been transmitted across generations and civilizations. Manuscript traditions reflect not only the written information they contain but also the materials, scripts, production techniques, artistic practices, social structures, and historical environments associated with their creation and use. Understanding this broader context provides an important foundation for applying computational technologies to historical documents.
A central focus is the application of deep learning to manuscript image analysis and OCR. Traditional optical character recognition can encounter substantial difficulties when working with historical documents because of faded ink, irregular characters, damaged pages, background noise, variable layouts, unusual scripts, and differences between historical and contemporary writing conventions. Deep neural networks and learned visual representations provide alternative approaches for extracting meaningful textual information from challenging document images.
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California Books
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