no code implementations • 26 May 2023 • Philippe Bernet, Joseph Chazalon, Edwin Carlinet, Alexandre Bourquelot, Elodie Puybareau
Linear objects convey substantial information about document structure, but are challenging to detect accurately because of degradation (curved, erased) or decoration (doubled, dashed).
no code implementations • 20 Feb 2023 • Solenn Tual, Nathalie Abadie, J Chazalon, Bertrand Duménieu, Edwin Carlinet
Our results show that while nested NER approaches enable extracting structured data directly, they do not benefit from the extra knowledge provided during training and reach a performance similar to the base approach on flat entities.
no code implementations • 17 Feb 2023 • Bertrand Duménieu, Edwin Carlinet, Nathalie Abadie, Joseph Chazalon
When extracting structured data from repetitively organized documents, such as dictionaries, directories, or even newspapers, a key challenge is to correctly segment what constitutes the basic text regions for the target database.
1 code implementation • 27 May 2021 • Joseph Chazalon, Edwin Carlinet, Yizi Chen, Julien Perret, Bertrand Duménieu, Clément Mallet, Thierry Géraud, Vincent Nguyen, Nam Nguyen, Josef Baloun, Ladislav Lenc, Pavel Král
Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy.
no code implementations • 6 Jan 2021 • Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret
Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task.