Methods and Systems for Identifying Digital Image Characteristics

• US 20090016611A1
• Filed: 07/10/2007
• Published: 01/15/2009
• Est. Priority Date: 07/10/2007
• Status: Active Grant

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Campbell, Richard John
• Granted Patent: US 8,340,430 B2
• Time in Patent Office: Days
• US Class Current: 382/199
• CPC Class Codes: G06V 10/242 by image rotation, e.g. by 90 degrees

Abstract

Aspects of the present invention relate to methods and systems for determining image characteristics in a digital image.

Claims

1. A method for determining an image characteristic in a digital image, said method comprising:

a) forming a binary image from a digital image;

b) determining a glyph component in said binary image;

c) determining a reference location for said glyph component;

d) determining a feature location in said glyph component;

e) determining a position, relative to said reference location, of feature location; and

f) determining an image characteristic based on said position.

2. A method according to claim 1, wherein said reference location is the centroid of said glyph component.

3. A method according to claim 1, wherein said determining a reference location comprises:

a) determining a bounding box for said glyph component; and

b) determining the center of said bounding box.

4. A method according to claim 1, wherein said determining a position comprises a quadrant decomposition based on said reference location.

5. A method according to claim 1, wherein said image characteristic is an orientation characteristic.

6. A method according to claim 1, wherein said image characteristic is a language characteristic.

7. A method for determining an image characteristic in a digital image, said method comprising:

a) forming a binary image from a digital image;

b) determining a first glyph component in said binary image;

c) determining a first reference location for said first glyph component;

d) in said first glyph component, determining a first feature location associated with a first feature;

e) determining a first position, relative to said first reference location, of said first feature location;

f) accumulating, in a first distribution counter, a frequency-of-occurrence count related to said first feature and said first position; and

g) determining an image characteristic based on a distribution feature, wherein said distribution feature is based on said first distribution counter.

8. A method according to claim 7 further comprising determining a confidence level associated with said image characteristic.

9. A method according to claim 7, wherein said determining an image characteristic comprises inputting said distribution feature to a classifier which has been previously trained.

10. A method according to claim 7 further comprising:

a) in said first glyph component, determining a second feature location associated with a second feature;

b) determining a second position, relative to said first reference location, of said second feature location; and

c) accumulating, in said first distribution counter, a frequency-of-occurrence count related to said second feature and said second position.

11. A method according to claim 7, wherein said first reference location is the centroid of said first glyph component.

12. A method according to claim 7, wherein said determining a first reference location comprises:

a) determining a first bounding box for said first glyph component; and

b) determining the center of said first bounding box.

13. A method according to claim 7, wherein said determining a first position comprises a quadrant decomposition based on said first reference location.

14. A method according to claim 7, wherein said image characteristic is an orientation characteristic.

15. A method according to claim 7, wherein said image characteristic is a language characteristic.

16. A method according to claim 7 further comprising:

a) determining a second glyph component in said binary image;

b) determining a second reference location for said second glyph component;

c) in said second glyph component, determining a second feature location associated with said first feature;

d) determining a second position, relative to said second reference location, of said second feature location; and

e) accumulating, in a second distribution counter, a frequency-of-occurrence count related to said first feature and said second position.

17. A method according to claim 16 further comprising combining said second distribution counter and said first distribution counter to form said distribution feature.

18. A system for determining an image characteristic in a digital image, said system comprising:

a) a binary-image generator for forming a binary image from a digital image;

b) a glyph-component determiner for determining a glyph component in said binary image;

c) a reference-location determiner for determining a reference location for said glyph component;

d) a feature locator for determining a feature location in said glyph component;

e) a position determiner for determining a position, relative to said reference location, of feature location; and

f) an image-characteristic determiner for determining an image characteristic based on said position.

19. A system according to claim 18, wherein said image characteristic is an orientation characteristic.

20. A system according to claim 18, wherein said image characteristic is a language characteristic.