Speaker
Description
Handwritten Text Recognition assumes what encrypted historical manuscripts withhold: a known script, and labelled examples of it. Two questions therefore come first. Which script family does this document belong to, and which symbols does it actually use? Both are still answered by hand, an expert-bound process that does not scale to collections of hundreds of manuscripts. This talk reports what happens when standard computer vision is turned on those questions, and why it fails in an instructive way. An unsupervised pipeline that segments, embeds and matches symbols against a database of reference alphabets assigns a cipher to the wrong alphabet with 76% confidence, even though its true script is absent from that database altogether. The winning reference is simply the only one cropped from a real manuscript instead of rendered from a font. Style, not shape, dominates the embedding space. I trace the consequences of that diagnosis: adversarial domain alignment and inference-time style adaptation, which yield a label-free script fingerprint consistent with palaeographic knowledge; the same entanglement along a temporal axis, across a millennium of Greek letterforms; and style deliberately reused as signal, for scribal attribution and dating.