@InProceedings{KuboNazAguOliDua:2018:UsUNPr,
author = "Kubo, Diandra Akemi and Nazare, Tiago Santana de and Aguirre,
Priscila Louise Ribeiro and Oliveira, Bruno Domingues and Duarte,
Felipe Sim{\~o}es Lage Gomes",
affiliation = "{Data Science Team - Itau Unibanco} and {Data Science Team - Itau
Unibanco} and {Data Science Team - Itau Unibanco} and {Data
Science Team - Itau Unibanco} and {Data Science Team - Itau
Unibanco}",
title = "The usage of U-Net for pre-processing document images",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "#deep-learning #computer-vision #image-processing.",
abstract = "When processing documents in real-world scenarios, it is common to
deal with artifacts that may hamper document analysis, such as
stamps, noise and strange backgrounds. Aiming to mitigate these
problems, we propose the use of U-Net, a very successful
biomedical image segmentation network, for handwritten and machine
text segmentation. In order to do so, we trained a model for each
type of text. One of the main advantages presented is that the
models are trained on artificial data, avoiding the wearisome task
of data labeling. For the machine text segmentation model, we test
its impacts on both word and character recognition when combined
with the Tesseract OCR model. For the handwritten segmentation
model, we present qualitative results. Initial experiments
indicate that both models are able to improve results in their
respective applications.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
language = "en",
ibi = "8JMKD3MGPAW/3S4ELD8",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S4ELD8",
targetfile = "sibgrapi_pi_cv.pdf",
urlaccessdate = "2024, Apr. 30"
}