Modelling Medieval Hands: Practical OCR for Caroline Minuscule
Brandon W. Hawk, Antonia Karaisl, Nick White · Digital humanities quarterly · 2019
Over the past few decades, the ever-expanding media of the digital world, including digital humanities endeavors, have become more reliant on the results of Optical Character Recognition (OCR) software. Yet, unfortunately, medievalists have not had as much success with using OCR software on handwritten manuscripts as scholars using printed books as their sources. While some projects to ameliorate this situation have emerged in recent years, using software to create machine-readable results from medieval manuscripts is still in its infancy. This article presents the results of a series of successful experiments with open-source neural network OCR software on medieval manuscripts. Results over the course of these experiments yielded character and word accuracy rates over 90%, reaching 94% and 97% accuracy in some instances. Such results are not only viable for creating machine-readable texts but also pose new avenues for bringing together manuscript studies and digital humanities in ways previously unrealized. A closer examination of the experiments indicates regular patterns among the OCR results that could potentially allow for use cases beyond pure text recognition, such as for paleographic classifications of script types.