@InProceedings{Correa:2021:CoOpCh,
               author = "Correa, Iago Louren{\c{c}}o",
          affiliation = "{Federal University of Rio Grande (FURG)}",
                title = "Combination of Optical Character Recognition Engines for Documents 
                         Containing Sparse Text and Alphanumeric Codes",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "optical character recognition, classifier combination, pattern 
                         recognition, tesseract, median string.",
             abstract = "Many companies that buy machines, parts, or tools retain documents 
                         such as notes, receipts, forms, or instruction manuals over the 
                         years, and they may find themselves in need of digitizing these 
                         accumulated documents. Thus, when using optical character 
                         recognition (OCR) systems in these documents, it is possible to 
                         note that these systems can present two main difficulties. The 
                         first is to locate the sparse text in a non-continuous way, and 
                         the second is to match words that are closer to codes and less to 
                         words in human language. Although there are many works in the 
                         literature about sparse texts, such as forms and tables, there is 
                         usually not much concern about the issue with codes in which one 
                         can not rely on dictionaries or even both problems together. 
                         Therefore, to correct this issue without having to search for 
                         extensive databases or conduct training and development of new 
                         models, this work proposed to take advantage of pre-trained models 
                         of OCR such as from the Tesseract engine or the Google Cloud's 
                         Vision API. In order to do so, we proposed the exploration of 
                         combination strategies, including a new one based on median 
                         string. The experimental results achieved up to 3.09% improvement 
                         in character accuracy and 1.16% in word accuracy in comparison to 
                         the best individual performances from the engines when our method 
                         based on string combination was adopted.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00048",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00048",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45BRTJ8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45BRTJ8",
           targetfile = "Paper ID 28.pdf",
        urlaccessdate = "2024, May 06"
}