Artificial Intelligence for Calligraphic Writer Identification: The Case of Lope de Vega’s Autographs

This paper presents a computational approach for detecting the calligraphic footprint of a scribe in a large documentary corpus. The system leverages advances in HTR (Handwritten Text Recognition) techniques, usually employed for automatic transcription, but on this occasion used to locate the specific handwriting of interest when dealing with an extensive collection of texts, in which there may be dozens or even hundreds of different hands. We conducted a control experiment with Lope de Vega (a renowned 17th-century Spanish playwright) and the Transkribus platform (user-friendly for researchers who are not computer specialists), obtaining very accurate results: once trained on Lope’s hand and on two hundred other distinct hands, the system can single out Lope’s handwriting in documents beyond the mod el, with high success rates (accuracy, precision, recall, and F1 scores in the range of 0.95-1.00). These findings pave the way for training models for hands of particular interest (authors, censors, copyists, bureaucrats, actors, etc.) and systematically scanning extant documents in order to detect other instances in which they participated, which could lead to discoveries of historical, literary, and patrimonial significance