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

2026-06-25

AI Summary

This work addresses the challenge of automatically deciphering historical encrypted manuscripts, where conventional two-stage pipelines often fail due to transcription errors that propagate into the decryption stage. To overcome this limitation, we propose the first end-to-end image-to-plaintext decryption framework that bypasses intermediate transcription entirely, jointly modeling computer vision and sequence decoding within a unified architecture to prevent error accumulation. To facilitate training, we develop a large-scale synthetic data generation pipeline producing cipher-like sequences grounded in realistic visual and linguistic priors. Experiments on the Copiale cipher demonstrate that our approach significantly outperforms traditional two-stage methods, confirming the efficacy of end-to-end modeling in enhancing both robustness and overall decipherment performance.

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.

Problem

• encrypted handwritten documents
• automatic decipherment
• transcription errors
• historical manuscripts
• plaintext recovery

Innovation

• end-to-end decryption
• encrypted manuscript
• direct image decryption
• synthetic data generation
• joint transcription-decryption