@incollection{mara17b,
          editor = {Hannah Busch and Franz Fischer and Patrick Sahle},
           title = {Automatable Annotations ? Image Processing and Machine Learning for Script in 3D and 2D with GigaMesh},
            year = {2017},
           pages = {137--149},
          author = {Bartosz Bogacz and Hubert Mara},
          volume = {11},
           month = {Juli},
       publisher = {Books on Demand},
       booktitle = {Kodikologie und Pal{\"a}ographie im Digitalen Zeitalter 4 ? Codicology and Palaeography in the Digital Age 4},
         address = {Norderstedt},
             url = {https://kups.ub.uni-koeln.de/7785/},
        abstract = {Libraries, archives and museums hold vast numbers of objects with script in 3D such as inscriptions, coins, and seals, which provide valuable insights into the history of humanity. Cuneiform tablets in particular provide access to information on more than three millennia BC. Since these clay tablets require an extensive examination for transcription, we developed the modular GigaMesh software framework to provide high-contrast visualization of tablets captured with 3D acquisiton techniques. This framework was extended to provide digital drawings exported as XML-based Scalable Vector Graphics (SVG), which are the fundamental input of our approach inspired by machine-learning techniques based on the principle of word spotting. This results in a versatile symbol-spotting algorithm to retrieve graphical elements from drawings enabling automated annotations. Through data homogenization, we achieve compatibility to digitally born manual drawings, as well as to retro-digitized drawings. The latter are found in large Open Access databases, e.g. provided by the Cuneiform Database Library Initiative (CDLI). Ongoing and future work concerns the adaptation of filtering and graphical query techniques for two-dimensional raster images widely used within Digital Humanities research.}
}