Visual-Pattern Detector v1.3 (VPD1.3)

Link:
Autor/in:
Verlag/Körperschaft:
Universität Hamburg
Erscheinungsjahr:
2021
Medientyp:
Software
Schlagworte:
  • Pattern Detection
  • Document Analysis
  • Pattern Recognition
  • Computer Vision
Beschreibung:

  • This software tool has been developed by Dr. Hussein Mohammed as a part of sub-project RFA05: Pattern Recognition in 2D Data from Digitised Images and Advanced Aquisition Techniques.

    What is new?

    • Better and more intuitive design
    • Bugs fixes

    Main usage:

    The main goal of this software tool is to automatically recognise and allocate visual patterns (such as words, drawings and seals) 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.

    Acknowledgement:

    The development of this software 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).

    I would like to thank the following colleagues for testing this software tool and validating the results: Dr. Giovanni Ciotti (CSMC), Dr. Volker Märgner (Technische Universität Braunschweig), Dr. Agnieszka Helman-Wazny (CSMC), Dr. Isabelle Marthot-Santaniello (Universität Basel).

  • {"references": ["Mohammed, H., M\u00e4rgner, V., & Ciotti, G. (2021). Learning-free pattern detection for manuscript research. International Journal on Document Analysis and Recognition (IJDAR), 1-13.", "Mohammed, H., Helman-Wazny, A., Colini, C., Beyer, W., Bosch, S. (2022). Pattern Analysis Software Tools (PAST) for Written Artefacts. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_15"]}
Beziehungen:
DOI 10.25592/uhhfdm.8832
Lizenzen:
  • https://creativecommons.org/licenses/by/4.0/legalcode
  • info:eu-repo/semantics/openAccess
Quellsystem:
Forschungsdatenrepositorium der UHH
Interne Metadaten
Quelldatensatz
oai:fdr.uni-hamburg.de:9186