Handwriting Analysis Tools

The field of computational palaeography has been developing for decades but it only recently starting to show some real promise. Some very good online articles are:

Here are some other tools you can use to play with manuscripts and their scripts:

Archetype Ink

As it describes itself on its Twitter bioarrow-up-right, Archetype (previously DigiPal) is "an integrated suite of web-based, open source tools for the study of medieval handwriting, art and iconography. The tool remains in development and though only an early version exists for Windows, a stable version exists for Mac and Ubuntu/ Linux. You install the program via Docker. Herearrow-up-right are instructions about how to do so and how to get started; more documentation on their Github sitearrow-up-right.

The tool is designed to identify letter forms, compare them, and allow you to pinpoint key features you can use to identify scripts.

HAT-2

One promising projectarrow-up-right is being worked on at the Centre for the study of Manuscript Culturesarrow-up-right at the University of Hamburg (Germany). As it describes itself in the manual, HAT-2 is "a software tool that can be used to analyse handwriting styles. Several different handwriting styles (scribal hands) can be analysed concurrently and sorted according to their similarity to a questioned or unknown style (query). A similarity score will be calculated for each predefined style (scribal hand) to create a relative comparison between them with respect to an unknown style."

You can download the installation file herearrow-up-right. This tool is designed for use on Windows computers, but can be used on a MacOS via a virtual machine or Winearrow-up-right. The manual with instructions for set up and how to use the tool is located in the zipfile.

You can find a very dense and complex explanation of the work underlying the Handwriting Analysis Tool (HAT) here:

H. Mohammed, V. Märgner, T. Konidaris, and H. S. Stiehl, “Normalised local näive bayes nearest-neighbour classifier for offline writer identification”, in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017, pp. 1013–1018.arrow-up-right

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