Learning to Decipher from Pixels -- A Case Study of Copiale

• URL: http://arxiv.org/abs/2604.23683v1
• Date: Sun, 26 Apr 2026 12:51:41 GMT
• Title: Learning to Decipher from Pixels -- A Case Study of Copiale
• Abstract summary: We propose an end-to-end, transcription-free approach that directly maps handwritten cipher images to plaintext.<n>We show that pretraining on generic handwriting data followed by cipher-specific fine-tuning substantially improves decipherment accuracy.<n>Our results demonstrate that transcription-free image-to-plaintext decipherment is both feasible and effective for historical substitution ciphers.
• Score: 4.238413656800081
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Historical encrypted manuscripts require both paleographic interpretation of cipher symbols and cryptanalytic recovery of plaintext. Most existing computational workflows rely on a transcription-first paradigm, in which handwritten symbols are transcribed prior to decipherment. This intermediate step is labor-intensive, error-prone, and not always aligned with the goal of direct plaintext recovery. We propose an end-to-end, transcription-free approach that directly maps handwritten cipher images to plaintext. Using the Copiale cipher as a case study, we introduce the first text-line-level dataset pairing cipher images with German plaintext. We show that pretraining on generic handwriting data followed by cipher-specific fine-tuning substantially improves decipherment accuracy. Our results demonstrate that transcription-free image-to-plaintext decipherment is both feasible and effective for historical substitution ciphers, offering a simplified and scalable alternative to traditional pipelines. https://github.com/leitro/Decipher-from-Pixels-Copiale

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