Semi-structured document image matching and recognition

Olivier Augereau; Nicholas Journet; Jean-Philippe Domenger

doi:10.1117/12.2003911

4 February 2013 Semi-structured document image matching and recognition

Olivier Augereau, Nicholas Journet, Jean-Philippe Domenger

Proceedings Volume 8658, Document Recognition and Retrieval XX; 865804 (2013) https://doi.org/10.1117/12.2003911
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

This article presents a method to recognize and to localize semi-structured documents such as ID cards, tickets, invoices, etc. Standard object recognition methods based on interest points work well on natural images but fail on document images because of repetitive patterns like text. In this article, we propose an adaptation of object recognition for image documents. The advantages of our method is that it does not use character recognition or segmentation and it is robust to rotation, scale, illumination, blur, noise and local distortions. Furthermore, tests show that an average precision of 97.2% and recall of 94.6% is obtained for matching 7 different kinds of documents in a database of 2155 documents.

Citation Download Citation

Olivier Augereau, Nicholas Journet, and Jean-Philippe Domenger "Semi-structured document image matching and recognition", Proc. SPIE 8658, Document Recognition and Retrieval XX, 865804 (4 February 2013); https://doi.org/10.1117/12.2003911

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available