[D-Scribes List] New releases of software tools

[Hussein Adnan Mohammed]

  Dear colleagues,
 
    The sub-project RFA05[1] from the Cluster of Excellence:   
"Understanding Written Artefacts[2]" in Universität Hamburg released  
new versions of the following software tools:
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HANDWRITING ANALYSIS TOOL (HAT):
HAT3.5: https://www.fdr.uni-hamburg.de/record/9179#.YK5TN6gzaUk

HAT 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.

More details about the method, the experimental results and the used  
datasets can be found in the following references:

/- H. Mohammed et al., "Normalised Local Naive Bayes Nearest-Neighbour  
Classifier for Offline Writer Identification", in Document Analysis  
and Recognition (ICDAR), 14th International Conference on. IEEE, 2017,  
Kyoto, Japan./
/- H. Mohammed et al., "Writer identification for historical  
manuscripts: Analysis and optimisation of a classifier as an  
easy-to-use tool for scholars from the humanities", in 2018 16th  
International Conference on Frontiers in Handwriting Recognition  
(ICFHR), pages 534–539. IEEE, 2018./
-----------------------------------------------
-----------------------------------------------
VISUAL-PATTERN DETECTION (VPD):
VPD1.3: https://www.fdr.uni-hamburg.de/record/9186#.YK5TMKgzaUk

VPD is a software tool that can be used to recognise and allocate  
visual patterns (such as words, drawings and seals) automatically in  
digitised manuscripts. The recall-precision balance of detected  
patterns can be controlled visually, and the detected patterns can be  
saved as annotations on the original images or as cropped images  
depending on the needs of users.

More details about the method, the experimental results and the used  
datasets can be found in the following reference:

/- //H. //Mohammed, //V. //Märgner, & //G. //Ciotti (2021).  
Learning-free pattern detection for manuscript research. International  
Journal on Document Analysis and Recognition (IJDAR), 1-13./

  -----------------------------------------------

    The main goal of these software tools is to help the scholars with  
their manuscript research, and the development of these tools was  
sponsored by the Cluster of Excellence 2176 ‘Understanding Written  
Artefacts’, generously funded by the German Research Foundation (DFG),  
within the scope of the work conducted at Centre for the Study of  
Manuscript Cultures (CSMC).


Links:
------
[1]  
https://www.csmc.uni-hamburg.de/written-artefacts/research/field-a/rfa05.html
[2] https://www.csmc.uni-hamburg.de/written-artefacts.html
  With best regards
Hussein
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Dr. rer. nat. Hussein Adnan Mohammed
https://www.csmc.uni-hamburg.de/about/people/mohammed.html 
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