Synthetic Document Images with Diverse Shadows for Deep Shadow Removal Networks.

Autor: Matsuo, Yuhi¹ (AUTHOR) aoki@elec.keio.ac.jp, Aoki, Yoshimitsu¹ (AUTHOR)

Publikováno v: Sensors (14248220). Jan2024, Vol. 24 Issue 2, p654. 19p.

Synthetic document generator for annotation-free layout recognition

Autor: Raman, Natraj, Shah, Sameena, Veloso, Manuela

Publikováno v: In Pattern Recognition August 2022 128

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Automatic Synthetic Document Image Generation using Generative Adversarial Networks: Application in Mobile-Captured Document Analysis

Autor: David Mollard, Quang Anh Bui, Salvatore Tabbone

Publikováno v: 2019 International Conference on Document Analysis and Recognition (ICDAR)
2019 International Conference on Document Analysis and Recognition (ICDAR), Sep 2019, Sydney, Australia. pp.393-400, ⟨10.1109/ICDAR.2019.00070⟩
ICDAR

International audience; In this paper, we propose a method using Generative Adversarial Networks for automatically synthesizing document images that are similar to real printed documents captured by mobile phone's camera in unconstrained environment.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fee6b888974a605a1ccfe7740c27996
https://hal.univ-lorraine.fr/hal-02965123

Similarity-Based Synthetic Document Representations for Meta-Feature Generation in Text Classification

Autor: Thiago Salles, Thierson Couto Rosa, Marcos André Gonçalves, Sergio Canuto

Publikováno v: SIGIR

We propose new solutions that enhance and extend the already very successful application of meta-features to text classification. Our newly proposed meta-features are capable of: (1) improving the correlation of small pieces of evidence shared by nei

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e62dd12eb37c9a396e827584650a6c06
https://doi.org/10.1145/3331184.3331239