Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers

TL;DR

This paper introduces the CSI metric, a new method for comparing ciphered manuscripts based on visual features, to assist scholars in cryptanalysis and transcription of historical handwritten ciphers.

Contribution

It proposes the CSI metric for unsupervised clustering of ciphered documents using visual features, aiding in the analysis of historical handwritten ciphers.

Findings

CSI metric effectively groups similar ciphered manuscripts

Visual features like SIFT and OCR descriptors improve clustering accuracy

Method supports cryptanalysis of historical handwritten ciphers

Abstract

Historical ciphered manuscripts are documents that were typically used in sensitive communications within military and diplomatic contexts or among members of secret societies. These secret messages were concealed by inventing a method of writing employing symbols from diverse sources such as digits, alchemy signs and Latin or Greek characters. When studying a new, unseen cipher, the automatic search and grouping of ciphers with a similar alphabet can aid the scholar in its transcription and cryptanalysis because it indicates a probability that the underlying cipher is similar. In this study, we address this need by proposing the CSI metric, a novel way of comparing pairs of ciphered documents. We assess their effectiveness in an unsupervised clustering scenario utilising visual features, including SIFT, pre-trained learnt embeddings, and OCR descriptors.