Clustering

• US 20030202709A1
• Filed: 04/25/2002
• Published: 10/30/2003
• Est. Priority Date: 04/25/2002
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Malvar, Henrique S., Simard, Patrice Y., Renshaw, Erin L.
• Granted Patent: US 7,164,797 B2
• Time in Patent Office: Days
• US Class Current: 382/243
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/18 Extraction of features or characteristics of the image
• CPC Class Codes: G06V 30/19013 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching specially adapted for image segmentation G06T7/10; specially adapted for the analysis of motion G06T7/20; specially adapted for image alignment G06T7/30; specially adapted for the calculation of depth from stereo images G06T7/50; specially adapted for position determination G06T7/70
• CPC Class Codes: G06V 30/40 Document-oriented image-based pattern recognition

Abstract

Systems and methods for performing clustering of a document image are disclosed. A property of an extracted mark from a document is compared to the properties of the existing clusters. If the property of the mark fails to match any of the properties of the existing clusters, the mark is added as a new cluster to the existing cluster. One property that can be utilized is x size and y size, which is the width and height, of the existing clusters. Another property that can be employed is ink size, which refers to the ratio of black pixels to total pixels in a cluster. Yet another property that can be utilized is a reduced mark or image, which is a pixel size reduced version the bitmap of the mark and/or cluster. The above properties can be employed to identify mismatches and reduce the number of bit by bit comparisons performed.

Claims

1. A clustering system comprising:

a mark extractor to extract a mark from a document;

a match component operative to compare at least one property of the mark to match properties of existing clusters of marks so as to identify matching existing clusters;

a two dimensional table that stores the existing clusters according to box size; and

a match symbol component operative to compare the mark to the matching existing clusters and identify a matching cluster.

2. The clustering system of claim 1, further comprising a library to store the existing clusters.

3. The system of claim 1, further comprising a table of match properties of the existing clusters.

4. The clustering system of claim 2, the library comprising a local library and a global library, the local library comprising existing clusters added from a current page of the document and the global library comprising existing clusters added from previous pages of the document.

5. The clustering system of claim 1, the mark extractor being operative to locate the mark and extract the mark from the document.

6. The clustering system of claim 1, the match properties comprising x sizes and y sizes for the existing clusters.

7. The clustering system of claim 1, the match properties comprising ink sizes for the existing clusters.

8. The clustering system of claim 1, the match properties comprising resized images for the existing clusters.

9. The clustering system of claim 6, the at least one property of the mark being x size and y size.

10. The clustering system of claim 7, the at least one property of the mark being ink size.

11. The clustering system of claim 8, the at least one property of the mark being a resized mark image.

12. The clustering system of claim 1, the match component operative to compute an acceptable range of properties.

13. The clustering system of claim 1, the match component further operative to add the mark as a new cluster on the mark mismatching the existing clusters.

14. The clustering system of claim 1, the match symbol component being further operative to add the mark to a set of matching marks of the most matching cluster.

15. The clustering system of claim 13, further comprising a library, the library being operative to store the existing clusters and to sort the existing clusters according to a frequency of occurrence for the existing clusters.

16. A photocopier employing the clustering system of claim 1.

17. A fax machine employing the clustering system of claim 1.

18. A digital camera employing the clustering system of claim 1.

19. An image encoding system employing the clustering system of claim 1.

20. A method of clustering comprising:

locating a mark within a document;

comparing a first property of the mark with first properties of existing clusters to identify matching and mismatching clusters;

on a match of the first property, comparing a bitmap of the mark with bitmaps of the matching clusters to find a matched cluster of the matching clusters; and

on a mismatch of the first property and on a mismatch of the bitmap, adding the mark as the new cluster to the existing clusters.

21. The method of claim 20, the first property being compared comprising at least one of the group comprising x size, y size, ink size and resized mark image.

22. The method of claim 20, further comprising generating a resized mark image from the bitmap of the mark.

23. The method of claim 22, the resized mark image generated by dividing the bitmap of the mark into 9 regions and using a majority of pixels in each of the 9 regions for corresponding pixels of a 9 pixel, 3 by 3, resized mark image.

24. The method of claim 20, further comprising computing a hot point for the mark.

25. The method of claim 20, further comprising comparing a second property of the mark with second properties of existing clusters to identify the matching and the mismatching clusters.

26. The method of claim 25, further comprising comparing a third property of the mark with third properties of existing clusters to identify the matching and the mismatching clusters.

27. The method of claim 20, wherein comparing a bitmap of the mark with bitmaps of the matching clusters comprises computing a center of gravity of the mark and a center of gravity of the cluster, computing an xor distance between the mark the matching clusters and selecting the most matched cluster having a smallest xor distance with the mark and the smallest xor distance being within an acceptable range.

28. The method of claim 20, wherein frequently occurring existing clusters are maintained in a global library and rarely occurring clusters are maintained in a local library.

29. A document encoding system comprising:

a mask separator operative to generate a binary mask from a document image, the binary mask including textual information;

a background foreground segmenter operative to segment a foreground image and a background image from the document image according to the binary mask; and

a clustering system operative to identify clusters in the mask in a computationally efficient manner.

30. A data packet adapted to be transmitted between at least two computer processes, comprising:

a data field comprising information associated with a property of clusters, the property of the clusters being efficiently comparable to a similar property of a mark to identify a mismatch, the mismatch indicating that the mark is a new cluster and avoiding a bit by bit comparison of the mark to the clusters.

31. A computer readable medium storing computer executable components operable to perform a method of clustering, comprising:

a component for locating a mark;

a component for comparing a first property of the mark with first properties of existing clusters to identify matching and mismatching clusters;

on a match of the first property, a component for comparing a bitmap of the mark with bitmaps of the matching clusters to find a most matched cluster of the matching clusters; and

on a mismatch of the first property and on a mismatch of the bitmap, a component to add the mark as a new cluster to the existing clusters.

32. A computer readable medium storing computer executable instructions operable to perform a method of clustering, comprising:

for each page of at least one page of a document;

a component for finding at least one mark;

a component for comparing a first property of the at least one mark with first properties of existing clusters to identify matching and mismatching clusters;

on a match of the first property, a component for comparing a bitmap of the at least one mark with bitmaps of the matching clusters to find a most matched cluster of the matching clusters; and

on a mismatch of the first property and on a mismatch of the bitmap, a component for adding the at least one mark as a new cluster to the existing clusters; and

a component for updating a global library.