Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images

Abstract

Historical ciphers contain a wide range of symbols from various symbol sets. Identifying the cipher alphabet is a prerequisite before decryption can take place and is a time-consuming process. In this work we explore the use of image processing for identifying the underlying alphabet in cipher images, and to compare alphabets between ciphers. The experiments show that ciphers with similar alphabets can be successfully discovered through clustering.