PT Unknown
AU Arnau Baro
   Jialuo Chen
   Alicia Fornes
   Beata Megyesi
TI Towards a generic unsupervised method for transcription of encoded manuscripts
BT 3rd International Conference on Digital Access to Textual Cultural Heritage
PY 2019
BP 73
EP 78
DI 10.1145/3322905.3322920
DE A. Baró; J. Chen; A. Fornés; B. Megyesi.
AB Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods.
ER