SOURCE-LINKED INTELLIGENCE
Unsupervised Domain Adaptation for Symbol Spotting in Historical Encrypted Manuscripts
The decipherment of historical encrypted manuscripts poses a fundamental challenge in Digital Humanities: before any transcription can begin, the symbol inventory of the underlying cipher alphabet must first be identified and characterized. We address this challenge through symbol spotting: given a candidate alphabet specified as a set of rendered font glyphs, the task is to determine whether and where its characters appear in an unseen handwritten document, without any labeled examples from the target script. The main difficulty lies in the domain gap between clean, digitally rendered font qu
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-07T07:54:42.000Z
First collected: 2026-09-20T20:52:10.320Z. This is not the publication date.