SMU Data Science Review
Abstract
Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in script style and document quality. Furthermore, transfer learning and data augmentation techniques, like rotation and random cropping, enhance model robustness by mimicking the natural variability of handwritten texts.
Recommended Citation
Lane, Robert L. Jr; Mirza, Rafia; and Slater, Robert
(2025)
"A Modern Approach to Classifying Medieval Latin Scripts,"
SMU Data Science Review: Vol. 9:
No.
1, Article 7.
Available at:
https://scholar.smu.edu/datasciencereview/vol9/iss1/7
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Artificial Intelligence and Robotics Commons, Data Science Commons, Digital Humanities Commons, Medieval Studies Commons
