TY - GEN
T1 - The Role of Artificial Intelligence and Pattern Recognition in the Authentication and Analysis of Historical Documents
T2 - 8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024
AU - Vargas-Murillo, Alfonso Renato
AU - Sotelo-Calderon, Abel Fernando
AU - Gómez-Zegarra, Juan Luis
AU - Zegarra-Ponce, Luis Roberto
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This systematic literature review explores the application of artificial intelligence (AI) and pattern recognition techniques in the authentication and analysis of historical documents. A comprehensive search strategy across three major academic databases (Scopus, IEEE Xplore, and ACM Digital Library) yielded 17 relevant studies published between 2007 and 2023. The selected studies demonstrate the potential of various AI techniques, including deep learning approaches, natural language processing, and unsupervised learning methods, for tasks such as handwritten text recognition, document restoration, and visual similarity clustering. The reviewed literature highlights successful applications in digital preservation, automatic transcription, and information extraction, showcasing the ability of AI and pattern recognition to automate and accelerate the processing of large collections of historical documents. However, challenges such as the scarcity of annotated datasets, the need for interdisciplinary collaboration, and the development of user-friendly interfaces remain important areas for future research and development. This review emphasizes the significance of collaborative and interdisciplinary efforts in addressing these challenges and unlocking the potential of AI and pattern recognition in preserving and studying our cultural heritage contained within historical documents.
AB - This systematic literature review explores the application of artificial intelligence (AI) and pattern recognition techniques in the authentication and analysis of historical documents. A comprehensive search strategy across three major academic databases (Scopus, IEEE Xplore, and ACM Digital Library) yielded 17 relevant studies published between 2007 and 2023. The selected studies demonstrate the potential of various AI techniques, including deep learning approaches, natural language processing, and unsupervised learning methods, for tasks such as handwritten text recognition, document restoration, and visual similarity clustering. The reviewed literature highlights successful applications in digital preservation, automatic transcription, and information extraction, showcasing the ability of AI and pattern recognition to automate and accelerate the processing of large collections of historical documents. However, challenges such as the scarcity of annotated datasets, the need for interdisciplinary collaboration, and the development of user-friendly interfaces remain important areas for future research and development. This review emphasizes the significance of collaborative and interdisciplinary efforts in addressing these challenges and unlocking the potential of AI and pattern recognition in preserving and studying our cultural heritage contained within historical documents.
KW - Artificial intelligence
KW - Digital humanities
KW - Document analysis
KW - Historical documents
KW - Optical character recognition
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85213314379&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-7710-5_58
DO - 10.1007/978-981-97-7710-5_58
M3 - Conference contribution
AN - SCOPUS:85213314379
SN - 9789819777099
T3 - Lecture Notes in Networks and Systems
SP - 759
EP - 768
BT - Inventive Communication and Computational Technologies - Proceedings of ICICCT 2024
A2 - Ranganathan, G.
A2 - Papakostas, George A.
A2 - Shi, Yong
Y2 - 14 June 2024 through 15 June 2024
ER -