Hand-written Text Recognition techniques withthe aim to automatically identify and transcribehand-written text have been applied to histor-ical sources including ciphers. In this paper,we compare the performance of two machinelearning architectures, an unsupervised methodbased on clustering and a deep learning methodwith few-shot learning. Both models are testedon seen and unseen data from historical cipherswith different symbol sets consisting of varioustypes of graphic signs. We compare the modelsand highlight their differences in performance,with their advantages and shortcomings.