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@InProceedings{BalreiraWalt:2017:HaSyPu,
               author = "Balreira, Dennis Giovani and Walter, Marcelo",
          affiliation = "{Institute of Informatics - Universidade Federal do Rio Grande do 
                         Sul} and {Institute of Informatics - Universidade Federal do Rio 
                         Grande do Sul}",
                title = "Handwriting Synthesis from Public Fonts",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "handwriting synthesis, public fonts.",
             abstract = "Handwriting synthesis generates renderings of text which look like 
                         they were written by a human but are in fact synthesized by a 
                         model. From an input sample of the desired handwriting, we 
                         introduce an algorithm that finds the best match between 
                         characters using as source for the output text the large 
                         collection of publicly available fonts designed to look like 
                         handwriting. For each character in the desired output text, we 
                         find the best match among the public fonts using a metric that 
                         matches both the shape and appearance of the input real character. 
                         Once we have the set of best characters we build the output 
                         sentence or paragraph by concatenation of individual characters. 
                         Our results show that even though human calligraphy is highly 
                         individual and specialized, visually similar renderings are 
                         possible for many applications that do not demand full similarity. 
                         On a user study with 12 subjects, our synthesis results were 
                         considered, on average, 71% similar to the input samples.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4960255.pdf",
        urlaccessdate = "2021, Jan. 26"
}


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