# Learning to Decipher from Pixels: A Case Study of Copiale

arXiv: https://arxiv.org/abs/2604.23683 · HTML: https://mlsanity.com/paper/2604.23683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition](https://mlsanity.com/paper/2005.13044/) (2020)
- [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016)
- [Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture](https://mlsanity.com/paper/1912.10308/) (2019)
- [Decipherment of Historical Manuscript Images](https://mlsanity.com/paper/1810.04297/) (2018)
- [Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers](https://mlsanity.com/paper/2410.21913/) (2024)
- [Practical Fine-Tuning of Autoregressive Models on Limited Handwritten Texts](https://mlsanity.com/paper/2503.19546/) (2025)

## Abstract (arXiv)

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