The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 30, 2021

Filed:

Mar. 04, 2019
Applicant:

Ccc Information Services Inc., Chicago, IL (US);

Inventors:

Neda Hantehzadeh, Chicago, IL (US);

Christoph Plenio, Batavia, IL (US);

Nazanin Makkinejad, Chicago, IL (US);

Ruxiao Bao, Mountain View, CA (US);

Assignee:

CCC INFORMATION SERVICES INC., Chicago, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/32 (2006.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06K 9/32 (2013.01); G06K 9/6259 (2013.01); G06K 9/6267 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06K 2209/01 (2013.01); G06K 2209/15 (2013.01);
Abstract

Techniques for optimizing vehicle license plate recognition in images and their decoding include training a set of convolutional neural networks (CNNs) by using images in which license plates are identified or labeled as a whole, rather than by license plate parts or key points, and rather than by the individual, segmented characters represented thereon. The trained CNNs may operate on target images of environments to localize images of license plates included therein and determine the issuing jurisdiction and/or ordered set of characters represented on detected license plates without utilizing character segmentation and/or per-character recognition techniques. As such, license plates depicted within target images are able to be detected and decoded with greater tolerances for lighting conditions, deformations or damages, occlusions, differing image resolutions, differing angles of capture, variations of other objects depicted within the images (such as dense or changing signage), etc.


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