A new database for image retrieval of camera filmed printed documents

Thibault Lelong, Marius Preda, Titus Zaharia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - Web3D 2022
Subtitle of host publication27th ACM Conference on 3D Web Technology
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450399142
DOIs
Publication statusPublished - 2 Nov 2022
Externally publishedYes
Event27th ACM Conference on 3D Web Technology, Web3D 2022 - Evry, France
Duration: 2 Nov 20224 Nov 2022

Publication series

NameProceedings - Web3D 2022: 27th ACM Conference on 3D Web Technology

Conference

Conference27th ACM Conference on 3D Web Technology, Web3D 2022
Country/TerritoryFrance
CityEvry
Period2/11/224/11/22

Keywords

  • Database
  • Document image retrieval
  • Image similarity

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