Positionally encoded document image analysis and labeling

• US 20050149865A1
• Filed: 01/06/2004
• Published: 07/07/2005
• Est. Priority Date: 01/06/2004
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Dang, Yingnong, Wang, Jian, Chen, Liyong
• Granted Patent: US 7,581,171 B2
• Time in Patent Office: Days
• US Class Current: 715/246
• CPC Class Codes: G06F 3/0321 by optically sensing the absolute position with respect to a regularly patterned surface forming a passive digitiser, e.g. pen optically detecting position indicative tags printed on a paper sheet constructional details of pen-shaped pointing devices G06F3/03545, G06F3/03542, G06F3/037
• CPC Class Codes: G06F 3/03542 Light pens for emitting or receiving light

Abstract

Disclosed embodiments of the invention relate to analyzing document images, which contain positionally encoded information, such as a maze-pattern watermark, and labeling the images based on a degree to which the document'"'"'s content, such as text, occludes the position-encoding information. Depending on the degree of such occlusion, it may not be possible to extract enough position-encoding bits from a camera-captured image of the document to determine the camera-captured image'"'"'s location within the document. An analysis-and-labeling module receives, as input, image data output by an image-generation-and-capturing module and off-line training data; performs analysis-and-labeling processing; and outputs image-label information. The results of document-analysis-and-labeling processing may be used for efficiently determining a location of a camera-captured image within a positionally encoded document.

Claims

1. A method of labeling a document image containing positionally encoded information to indicate an amount of encoded position information that can be extracted from the image, the method comprising:

obtaining the document image; and

labeling the image as being of a type selected from at least;

a first type containing substantially only positionally encoded information and substantially no document content, and a second type containing document content that occludes at least a portion of the positionally encoded information.

2. The method of claim 1, wherein obtaining a document image further comprises:

rendering an electronic document to a bitmap representation corresponding to a printed document.

3. The method of claim 1, wherein obtaining a document image further comprises:

processing a scanned paper document.

4. The method of claim 1, wherein the second type is divided into a plurality of subtypes that represent distinct degrees of occlusion of the positionally encoded information by the document content.

5. A method of labeling a camera-captured image containing positionally encoded information to indicate an amount of encoded position information that can be extracted from the image, the method comprising:

obtaining the camera-captured image; and

labeling the image as being of a type selected from at least;

a first type containing substantially only positionally encoded information and substantially no document content, and a second type containing document content that occludes at least a portion of the positionally encoded information.

6. The method of claim 5, wherein a support interval of gradient image histogram is used for determining whether the image is of the first type or of the second type.

7. The method of claim 6, further comprising:

applying a gradient operator to the image to obtain a gradient image.

8. The method of claim 7, wherein the gradient operator is the Sobel edge operator.

9. The method of claim 8, further comprising:

generating a histogram of the gradient image.

10. The method of claim 9, further comprising:

using a largest number along the x-axis of the histogram that has a non-zero value as the support interval of gradient image histogram.

11. The method of claim 6, wherein an offline training session and an online labeling session are used for determining whether the image is of the first type or of the second type.

12. The method of claim 11, wherein a threshold, for use in distinguishing between images of the first type and images of the second type, is selected based on results of the offline training session performed on training-data images.

13. The method of claim 12, wherein, during the online labeling session, the threshold is compared to a support interval of gradient image histogram of the image to determine whether the image is of the first type or of the second type.

14. A system that labels a document image containing positionally encoded information to indicate an amount of encoded position information that can be extracted from the image, the system comprising:

an image-generation-and-capturing module that obtains the document image; and

an analysis-and-labeling module that labels the image as being of a type selected from at least;

a first type containing substantially only positionally encoded information and substantially no document content, and a second type containing document content that occludes at least a portion of the positionally encoded information.

15. The system of claim 14, wherein the image-generation-and-capturing module renders an electronic document to a bitmap representation corresponding to a printed document.

16. The system of claim 14, wherein the image-generation-and-capturing module processes a scanned paper document.

17. The system of claim 14, wherein the second type is divided into a plurality of subtypes that represent distinct degrees of occlusion of the positionally encoded information by the document content.

18. A system that labels a camera-captured image containing positionally encoded information to indicate an amount of encoded position information that can be extracted from the image, the system comprising:

an image-generation-and-capturing module that obtains the camera-captured image; and

an analysis-and-labeling module that labels the image as being of a type selected from at least;

a first type containing substantially only positionally encoded information and substantially no document content, and a second type containing document content that occludes at least a portion of the positionally encoded information.

19. The system of claim 18, wherein a support interval of gradient image histogram is used for determining whether the image is of the first type or of the second type.

20. The system of claim 19, wherein the analysis-and-labeling module applies a gradient operator to the image to obtain a gradient image.

21. The system of claim 20, wherein the gradient operator is the Sobel edge operator.

22. The system of claim 19, wherein the analysis-and-labeling module generates a histogram of the gradient image.

23. The system of claim 22, wherein the analysis-and-labeling module uses a largest number along the x-axis of the histogram that has a non-zero value as the support interval of gradient image histogram.

24. The system of claim 23, wherein the analysis-and-labeling module performs an offline training session and an online labeling session to determine whether the image is of the first type or of the second type.

25. The system of claim 24, wherein a threshold, for use in distinguishing between images of the first type and images of the second type, is selected based on results of the offline training session performed on training-data images.

26. The system of claim 25, wherein, during the online session, the analysis-and-labeling module compares the threshold to a support interval of gradient image histogram of the image to determine whether the image is of the first type or of the second type.

27. A computer-readable medium containing computer-readable instructions for labelling a document image containing positionally encoded information to indicate an amount of encoded position information that can be extracted from the image, by performing steps comprising:

obtaining the document image; and

labeling the image as being of a type selected from at least;

a first type containing substantially only positionally encoded information and substantially no document content, and a second type containing document content that occludes at least a portion of the positionally encoded information.

28. The computer-readable medium of claim 27, wherein obtaining a document image further comprises:

rendering an electronic document to a bitmap representation corresponding to a printed document.

29. The computer-readable medium of claim 27, wherein obtaining a document image further comprises:

processing a scanned paper document.

30. The computer-readable medium of claim 27, wherein the second type is divided into a plurality of subtypes that represent distinct degrees of occlusion of the positionally encoded information by the document content.

31. A computer-readable medium containing computer-readable instructions for labelling a camera-captured image containing positionally encoded information to indicate an amount of encoded position information that can be extracted from the image, by performing steps comprising:

obtaining the camera-captured image; and

labeling the image as being of a type selected from at least;

a first type containing substantially only positionally encoded information and substantially no document content, and a second type containing document content that occludes at least a portion of the positionally encoded information.

32. The computer-readable medium of claim 31, wherein a support interval of gradient image histogram is used for determining whether the image is of the first type or of the second type.

33. The computer-readable medium of claim 32, containing further computer-executable instructions for performing steps comprising:

applying a gradient operator to the image to obtain a gradient image.

34. The computer-readable medium of claim 33, wherein the gradient operator is the Sobel edge operator.

35. The computer-readable medium of claim 34, containing further computer-executable instructions for performing steps comprising:

generating a histogram of the gradient image.

36. The computer-readable medium of claim 35, containing further computer-executable instructions for performing steps comprising:

using a largest number along the x-axis of the histogram that has a non-zero value as the support interval of gradient image histogram.

37. The computer-readable medium of claim 32, wherein an offline training session and an online labeling session are used for determining whether the image is of the first type or of the second type.

38. The computer-readable medium of claim 37, wherein a threshold, for use in distinguishing between images of the first type and images of the second type, is selected based on results of the offline training session performed on training-data images.

39. The computer-readable medium of claim 38, wherein, during the online labeling session, the threshold is compared to a support interval of gradient image histogram of the image to determine whether the image is of the first type or of the second type.