arXiv — NLP / Computation & Language · · 3 min read

Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2606.27700 (cs)
[Submitted on 26 Jun 2026]

Title:Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline

View a PDF of the paper titled Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline, by Marino Oliveros-Blanco and 3 other authors
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Abstract:Historical encrypted manuscripts present a challenging problem at the intersection of cryptology, linguistics, paleography, and computer vision. Current automatic decipherment approaches usually rely on a two-stage pipeline: transcription of cipher symbols from manuscript images, followed by decryption into plaintext. However, this design is sensitive to transcription errors, which propagate to the final output. We present Direct Image Decryption, an end-to-end approach that directly maps encrypted manuscript images to plaintext, bypassing the intermediate transcription stage. Using the Copiale cipher as a case study, we build a synthetic data generation pipeline to create large-scale cipher-like training data and compare the traditional pipeline with the proposed joint architecture. Results show that joint image-to-plaintext modeling is a promising alternative to traditional transcription-based pipelines.
Comments: Published at HistoCrypt 2026 (9th International Conference on Historical Cryptology). NEALT Proceedings Series Number 61. Tartu University Library. 10 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2606.27700 [cs.CV]
  (or arXiv:2606.27700v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2606.27700
arXiv-issued DOI via DataCite (pending registration)
Journal reference: NEALT Proceedings Series Number 61, Tartu University Library, 2026. ISSN 1736-6305

Submission history

From: Marino Oliveros Blanco [view email]
[v1] Fri, 26 Jun 2026 03:57:30 UTC (1,816 KB)
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