System and method for detecting text in real-world color images

• US 20070110322A1
• Filed: 09/05/2006
• Published: 05/17/2007
• Est. Priority Date: 09/02/2005
• Status: Active Grant

• Current Assignee: The Blindsight Corporation
• Original Assignee: The Blindsight Corporation
• Inventors: Lagerstrom, Stellan, Yuille, Alan, Terry, Daniel, Chen, Xiangrong, Nitzberg, Mark
• Granted Patent: US 7,817,855 B2
• Time in Patent Office: Days
• US Class Current: 382/227
• CPC Class Codes: G06V 20/62 Text, e.g. of license plates, overlay texts or captions on TV images
• CPC Class Codes: G06V 20/63 Scene text, e.g. street names
• CPC Class Codes: G06V 30/10 Character recognition

Abstract

A method and apparatus for detecting text in real-world images comprises calculating a cascade of classifiers, the cascade comprising a plurality of stages, each stage including one or more weak classifiers, the plurality of stages organized to start out with classifiers that are most useful for ruling out non-text regions, and removing regions classified as non-text regions from the cascade prior to completion of the cascade, to further speed up processing.

Claims

1. A method of detecting text in real-world images comprising:

calculating a cascade of classifiers, the cascade comprising a plurality of stages, each stage including one or more weak classifiers;

the plurality of stages organized to start out with classifiers that are most useful for ruling out non-text regions; and

removing regions classified as non-text regions from the cascade prior to completion of the cascade, to further speed up processing.

2. The method of claim 1, wherein the cascade comprises seven AdaBoost layers.

3. The method of claim 2, wherein each layer of the cascade has an equal or greater number of classifiers than each previous layer of the cascade.

4. The method of claim 2, wherein the classifiers in layers are secondarily ordered based on speed of computation.

5. The method of claim 1, further comprising:

outputting the data as identified text regions, separated from non-text regions.

6. The method of claim 1, further comprising utilizing a binarization process including:

classifying individual pixels as one of;

non-text, light potential-text, and dark potential-text.

7. The method of claim 6, further comprising utilizing two neighborhood thresholds:

TLight=μ

+kσ and

TDark=μ

−

kσ

where and μ and

σ

are the mean and variance within the selected neighborhood respectively, and k is a constant.

8. The method of claim 6, further comprising:

grouping the pixels into connected components (cc'"'"'s) based on their classification and proximity.