@inproceedings{f5143e57d2f84556913a02c15d3d15c2,
title = "A new database for image retrieval of camera filmed printed documents",
abstract = "The massive use of phones and their cameras is driving the research around augmented reality technologies that can be used in a browser. Indeed, this could allow to turn every physical support into an access to digital information. A family of specific objects used for such scenario is the printed material. The applications augmenting printed material with additional content such as videos, 3d animations, sound, etc. follow the same scenario: the printed material is filmed by the camera phone and the captured image is sent to a server able to run image recognition algorithms in order to retrieve a similar image in a database. Several technological building-blocks are composing the pipeline including image segmentation (usually done on the phone to extract only the pixels corresponding to the printed material) and image recognition (usually performed on the server). New methods and tools are proposed every year to address them, however, there is still a lack of a common database to benchmark these new methods. In this paper, we propose such database that we make publicly available. url : https://github.com/Ttibo/A-new-database-for-image-retrieval-of-camera-filmed-printed-documents.",
keywords = "Database, Document image retrieval, Image similarity",
author = "Thibault Lelong and Marius Preda and Titus Zaharia",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 27th ACM Conference on 3D Web Technology, Web3D 2022 ; Conference date: 02-11-2022 Through 04-11-2022",
year = "2022",
month = nov,
day = "2",
doi = "10.1145/3564533.3564569",
language = "English",
series = "Proceedings - Web3D 2022: 27th ACM Conference on 3D Web Technology",
publisher = "Association for Computing Machinery, Inc",
editor = "Spencer, \{Stephen N.\}",
booktitle = "Proceedings - Web3D 2022",
}