PT Unknown
AU Giacomo Magnifico
   Beata Megyesi
   Mohamed Ali Souibgui
   Jialuo Chen
   Alicia Fornes
TI Lost in Transcription of Graphic Signs in Ciphers
BT International Conference on Historical Cryptology (HistoCrypt 2022)
PY 2022
BP 153
EP 158
DE transcription of ciphers; hand-written text recognition of symbols; graphic signs
AB Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings.
ER