Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyBalreiraWalt:2017:HaSyPu
TitleHandwriting Synthesis from Public Fonts
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size3849 KiB
Context area
Author1 Balreira, Dennis Giovani
2 Walter, Marcelo
Affiliation1 Institute of Informatics - Universidade Federal do Rio Grande do Sul
2 Institute of Informatics - Universidade Federal do Rio Grande do Sul
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 20:59:40 :: -> administrator ::
2020-02-19 02:01:33 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordshandwriting synthesis, public fonts.
AbstractHandwriting 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.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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