License plate character segmentation using likelihood maximization

• US 20130294652A1
• Filed: 05/04/2012
• Published: 11/07/2013
• Est. Priority Date: 05/04/2012
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Fan, Zhigang, Zhao, Yonghui, Burry, Aaron Michael, Kozitsky, Vladimir
• Granted Patent: US 9,014,432 B2
• Time in Patent Office: Days
• US Class Current: 382/105
• CPC Class Codes: G06V 20/625 License plates
• CPC Class Codes: G06V 20/63 Scene text, e.g. street names

Abstract

A method determines a license plate layout configuration. The method includes generating at least one model representing a license plate layout configuration. The generating includes segmenting training images each defining a license plate to extract characters and logos from the training images. The segmenting includes calculating values corresponding to parameters of the license plate and features of the characters and logos. The segmenting includes estimating a likelihood function specified by the features using the values. The likelihood function measures deviations between an observed plate and the model. The method includes storing a layout structure and the distributions for each of the at least one model. The method includes receiving as input an observed image including a plate region. The method includes segmenting the plate region and determining a license plate layout configuration of the observed plate by comparing the segmented plate region to the at least one model.

Claims

1. A method for determining a license plate layout configuration, the method comprising:

generating at least one model representing a license plate layout configuration, the generating including;

segmenting training images each defining a license plate to extract characters and logos from the training images,

calculating values corresponding to parameters of the license plate and features of the characters and logos, and

estimating a likelihood function specified by the features using the values, the likelihood function being adapted to measure deviations between an observed plate and the model;

storing a layout structure and the distributions for each of the at least one model;

receiving as input an observed image including a plate region;

segmenting the plate region; and

,

determining a license plate layout configuration of the observed plate by comparing the segmented plate region to the at least one model.

2. The method according to claim 1, wherein the estimating the likelihood function includes:

building a histogram for each feature; and

,

estimating the likelihood function as normalized histograms.

3. The method according to claim 1, wherein the segmenting includes:

(a) producing a binary map of the plate region by applying a threshold for binarization;

(b) determining object connected components relative to background components, wherein the object components include candidates for a character or a logo;

(c) matching the determined black connected components against the at least one model;

(d) determining if the matching score for the model is below a pre-set threshold; and

(e) determining the model that is a closest match to the plate region in the observed image.

4. The method according to claim 3, wherein the segmenting further includes:

measuring height and width features of each determined black connected component;

5. The method according to claim 3, wherein the segmenting further includes:

varying the threshold for binarization and repeating (b) through (e).

6. The method according to claim 1, wherein the segmenting includes:

(a) producing a binary map of the plate region;

(b) determining object components relative to background components;

(c) performing a projection in the vertical region;

(d) locating gaps between components by applying a threshold for valley detection;

(e) dividing the region into a sub-region at the gap;

(f) determining a collection of black pixels in each sub-region as including a candidate for a character or logo;

(g) matching the collections of black pixels against the at least one model;

(h) determining if a matching score for the model is below a pre-set threshold; and

(i) determining a model that is a closest match to the plate region in the observed image.

7. The method according to claim 6, wherein the segmenting further includes:

varying the threshold of valley detecting and repeating (d)-(i).

8. The method according to claim 1, wherein the calculating the features include character dimensions, character positions, logo dimensions, and logo positions.

9. The method according to claim 1, wherein the likelihood function is a likelihood of a set of the parameters of the license plate and the features of the characters and logos, the set being selected from the group consisting of:

character width;

character height;

distance between centers of characters in a group of characters;

a difference between left and right margins of the license plate;

logo height;

logo width;

logo positions relative to neighboring characters; and

,

a combination of the above.

10. The method according to claim 9, wherein the left margin is calculated as a distance between center of the first character of the plate to the left edge and the right margin is calculated as a distance between the center of the last character of the plate and the right edge.

11. A system for determining a license plate layout configuration, the system comprising:

a training device including a processor for generating at least one model representing a license plate layout configuration, the training device being adapted to;

segment training images of license plates to extract characters and logos from the training images,

calculate values corresponding to parameters of the license plate and features of the characters and logos,

and,

estimate a likelihood function specified by the features using the values, the likelihood function being adapted to measure deviations between an observed plate and the model;

a storage device in communication with the training device, the storage device being adapted to store a layout structure and the distributions for each model;

a segmentation device in communication with the storage device, wherein upon receipt of an observed image including a plate region, the segmentation device is adapted to;

compare the plate region to the at least one model stored in the storage device; and

,

based on the comparison, determine a license plate layout configuration of the observed.

12. The system according to claim 11, wherein the training device estimates the likelihood function as normalized histograms built for each of the features.

13. The system according to claim 11, wherein the segmentation device is adapted to:

produce a binary map of the plate region;

use the binary map to determine object connected components relative to background components;

match the determined black connected components against the at least one model;

determine if a matching score for the at least one model is below a pre-set threshold; and

,

determine the at least one model that is a closest match to the plate region in the observed image.

14. The system according to claim 13, wherein the segmentation device is adapted to produce and use at least a second binary map by varying a threshold for binarization.

15. The system according to claim 13, wherein the segmentation device is further adapted to:

measure height and width features of each determined black connected component.

16. The system according to claim 13, wherein after producing the binary map, the segmentation device is further adapted to:

perform a projection in the vertical region;

locate gaps between components by applying a threshold for valley detection;

divide the region into a sub-region at the gap; and

,

determine a collection of black pixels in each sub-region as including a candidate for a character or logo.

17. The system according to claim 16, wherein the gaps can be located by varying the threshold of valley.

18. A method for segmenting an observed image of a license plate for determining a layout configuration of the license plate, the method including:

providing at least one model representing a license plate layout configuration;

receiving as input an observed image including a plate region;

producing binary maps of the plate region by applying at least two binary thresholds;

using the binary maps to determine object connected components relative to background connected components;

matching the determined black connected components against the at least one model;

determining if the matching score for the at least one model is below a pre-set threshold; and

,

determining if the at least one model is a closest match to the plate region in the observed image.

19. A computer program product comprising tangible media which encodes instructions for performing the method of claim 18.

20. A system for determining a license plate configuration, the system comprising:

a segmentation device comprising memory which stores instructions for performing the method of claim 18 and a processor, in communication with the memory for executing the instructions.