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Author Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes edit   pdf
url  openurl
  Title Lost in Transcription of Graphic Signs in Ciphers Type Conference Article
  Year 2022 Publication International Conference on Historical Cryptology (HistoCrypt 2022) Abbreviated Journal  
  Volume Issue Pages 153-158  
  Keywords (up) transcription of ciphers; hand-written text recognition of symbols; graphic signs  
  Abstract 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.  
  Address Amsterdam, Netherlands, June 20-22, 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
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  Area Expedition Conference HystoCrypt  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ MBS2022 Serial 3731  
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