# Decipherment of Historical Manuscript Images

arXiv: https://arxiv.org/abs/1810.04297 · HTML: https://mlsanity.com/paper/1810.04297/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2107.10064/) (2021)
- [One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2105.05300/) (2021)
- [General Detection-based Text Line Recognition](https://mlsanity.com/paper/2409.17095/) (2024)
- [Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers](https://mlsanity.com/paper/2410.21913/) (2024)
- [Determination of language families using deep learning](https://mlsanity.com/paper/2409.02393/) (2024)
- [Learning to Decipher from Pixels: A Case Study of Copiale](https://mlsanity.com/paper/2604.23683/) (2026)
- [Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline](https://mlsanity.com/paper/2606.27700/) (2026)

## Abstract (arXiv)

European libraries and archives are filled with enciphered manuscripts from the early modern period. These include military and diplomatic correspondence, records of secret societies, private letters, and so on. Although they are enciphered with classical cryptographic algorithms, their contents are unavailable to working historians. We therefore attack the problem of automatically converting cipher manuscript images into plaintext. We develop unsupervised models for character segmentation, character-image clustering, and decipherment of cluster sequences. We experiment with both pipelined and joint models, and we give empirical results for multiple ciphers.