Decipherment of Historical Manuscript Images
This addresses the challenge of making enciphered historical manuscripts accessible to historians, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of automatically converting cipher manuscript images into plaintext for historical documents, achieving empirical results for multiple ciphers through unsupervised models for character segmentation, clustering, and decipherment.
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.