Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers
This addresses a domain-specific need for scholars analyzing historical ciphered manuscripts, but it is incremental as it builds on existing visual feature methods.
The study tackled the problem of automatically grouping historical handwritten ciphers with similar alphabets to aid transcription and cryptanalysis, proposing the CSI metric and achieving effectiveness in unsupervised clustering using visual features.
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.