Exploring the Automatic Alphabet Identification of Images of Handwritten Ciphers

Abstract

Historical encrypted manuscripts often use invented or heterogeneous alphabets, making alphabet identification a necessary but traditionally manual first step prior to transcription and decryption. This work explores the use of unsupervised computer vision methods to automate this task without requiring labeled data. We propose a pipeline that segments characters from cipher manuscripts, groups them into clusters of visually similar symbols using unsupervised methods, and compares those clusters against a reference database of known alphabet symbols to identify the most likely underlying writing system. Experiments show that the method can correctly identify the alphabet when a handwritten alphabet is available, but performance degrades when handwritten symbols are compared against printed alphabets, with handwriting style dominating shape similarity. These results highlight the importance of realistic handwritten reference alphabets.