<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper deals with historical encrypted manuscripts and introduces an automated method for the detection and transcription of ciphertext symbols for subsequent cryptanalysis. Our database contains documents used in the past by aristocratic families living in the territory of Slovakia. They are encrypted using a nomenclator which is a specific type of substitution cipher. In our case, the nomenclator uses digits as ciphertext symbols. We have proposed a method for the detection, classification, and transcription of handwritten digits from the original documents. Our method is based on Mask R-CNN which is a deep convolutional neural network for instance segmentation. Mask R-CNN was trained on a manually collected database of digit annotations. We employ a specific strategy where the input image is first divided into small blocks. The image blocks are then passed to Mask R-CNN to obtain detections. This way we avoid problems related to the detection of a large number of small dense objects in a high-resolution image. Experiments have shown promising detection performance for all digit types with minimum false detections.</p>
</abstract>

This paper deals with historical encrypted manuscripts and introduces an automated method for the detection and transcription of ciphertext symbols for subsequent cryptanalysis. Our database contains documents used in the past by aristocratic families living in the territory of Slovakia. They are encrypted using a nomenclator which is a specific type of substitution cipher. In our case, the nomenclator uses digits as ciphertext symbols. We have proposed a method for the detection, classification, and transcription of handwritten digits from the original documents. Our method is based on Mask R-CNN which is a deep convolutional neural network for instance segmentation. Mask R-CNN was trained on a manually collected database of digit annotations. We employ a specific strategy where the input image is first divided into small blocks. The image blocks are then passed to Mask R-CNN to obtain detections. This way we avoid problems related to the detection of a large number of small dense objects in a high-resolution image. Experiments have shown promising detection performance for all digit types with minimum false detections.

Automated Transcription of Historical Encrypted Manuscripts

Slovak University of Technology in Bratislava

Tatra Mountains Mathematical Publications

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Automated Transcription of Historical Encrypted Manuscripts"}

This paper deals with historical encrypted manuscripts and introduces an automated method for the detection and transcription of ciphertext symbols for...

历史加密手稿的自动抄写

在线发布:2023年2月15日

页面范围： 65 - 86

已接收:2022年10月2日

内政部： https://doi.org/10.2478/tmmp-2022-0019

关键词
历史密码，命名器，手稿，转录，机器学习，深度卷积神经网络，屏蔽R-CNN

©2022 Eugen Antal等人，Sciendo出版

本作品根据知识共享署名非商业NoDerivatives 4.0国际许可证获得许可。