@InProceedings{GonçalvesMenoSchw:2015:LiPlCh,
               author = "Gon{\c{c}}alves, Gabriel Resende and Menotti, David and Schwartz, 
                         William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Ouro Preto} and {Universidade Federal de Minas Gerais}",
                title = "License plate character segmentation using Partial Least Squares",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Rios, Ricardo Araujo and Paiva, Afonso",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Automatic license plate recognition, character segmentation, 
                         partial least squares.",
             abstract = "A very important research topic nowadays is the Automatic License 
                         Plate Recognition (ALPC). This task consists in locating and 
                         identifying an on-track vehicle automatically. This task can be 
                         divided into the following subtasks: vehicle detection, license 
                         plate detection, characters segmentation and character 
                         recognition. This work proposes a new technique to perform 
                         character segmentation, which is considered solved in the 
                         literature, but in practice is a bottleneck for achieving a robust 
                         ALPC system. Our approach is a learning-based technique that uses 
                         a regression method known as Partial Least Squares to find the 
                         best points where the segmentation should be done between the 
                         characters. We perform experiments using a dataset composed of 
                         2,000 license plates and three baselines to compare them with the 
                         results obtained by the proposed approach. In addition, we 
                         evaluate the usage of the PLS with five feature descriptors and 
                         our results show that our method is able to achieve a result up to 
                         46.5% of accuracy, evaluated by the Jaccard measure.",
  conference-location = "Salvador, BA, Brazil",
      conference-year = "26-29 Aug. 2015",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3JRK5BH",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JRK5BH",
           targetfile = "2015-Sibgrapi-SegPlate.pdf",
        urlaccessdate = "2024, Apr. 28"
}