Mohamed Ali Souibgui author Pau Torras author Jialuo Chen author Alicia Fornes author 2023 This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage. DAG exported from refbase (http://refbase.cvc.uab.es/show.php?record=3849), last updated on Fri, 10 Nov 2023 17:07:13 +0100 text https://doi.org/10.1145/3604951.3605509 https://doi.org/10.1145/3604951.3605509 Admin @ si @ STC2023 HIP 2023 conference publication 7 12