Methods and systems for automatic detection of continuous-tone regions in document images
First Claim
Patent Images
1. An image segmentation method, said method comprising:
- a) obtaining pixel attribute data for a mixed-content image;
b) identifying a text region in said image;
c) identifying a background region in said image;
d) analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
e) analyzing said contone regions to identify any text regions present within said contone regions;
f) analyzing said contone regions to identify any background regions present in said contone regions;
g) analyzing areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions; and
h) repeating steps e-g until no further sub-regions are found.
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Abstract
Embodiments of the present invention comprise methods and systems for segmentation of digital mixed-content documents.
76 Citations
26 Claims
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1. An image segmentation method, said method comprising:
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a) obtaining pixel attribute data for a mixed-content image;
b) identifying a text region in said image;
c) identifying a background region in said image;
d) analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
e) analyzing said contone regions to identify any text regions present within said contone regions;
f) analyzing said contone regions to identify any background regions present in said contone regions;
g) analyzing areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions; and
h) repeating steps e-g until no further sub-regions are found. - View Dependent Claims (2, 3, 4, 5, 6)
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7. An image segmentation method, said method comprising:
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a) obtaining pixel attribute data for a mixed-content image, said pixel attribute data comprising at least one of a luminance data, a chrominance data and a hue data;
b) downsampling said pixel data;
c) filtering said pixel data to remove noise;
d) computing a local feature to identify a text region in said image;
e) analyzing a luminance histogram of said image to identify a background region in said image;
f) labeling any background regions as such;
g) applying morphological processing to said background regions and said text regions;
h) analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
i) analyzing said contone regions to identify any text regions present within said contone regions;
j) analyzing said contone regions to identify any background regions present in said contone regions;
k) analyzing areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions;
l) repeating steps e-g until no further sub-regions are found; and
m) analyzing said contone regions and said contone sub-regions to identify pictorial contone regions. - View Dependent Claims (8, 9)
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10. An image segmentation method, said method comprising:
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a) obtaining pixel attribute data for a mixed-content image, said pixel attribute data comprising at least one of a luminance data, a chrominance data and a hue data;
b) downsampling said pixel data;
c) filtering said pixel data to remove noise;
d) computing a local discriminating feature to identify a text region in said image;
e) analyzing a luminance histogram of said image to identify a background region in said image;
f) labeling any background regions as such;
g) analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
h) analyzing said contone regions to identify any text regions present within said contone regions;
i) analyzing said contone regions to identify any background regions present in said contone regions;
j) analyzing areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions;
k) repeating steps e-g until no further sub-regions are found; and
l) analyzing said contone regions and said contone sub-regions to identify contone regions and non-contone regions. - View Dependent Claims (11, 12)
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13. An image segmentation method, said method comprising:
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a) obtaining pixel attribute data for a mixed-content image, said pixel attribute data comprising at least one of a luminance data, a chrominance data and a hue data;
b) downsampling said pixel data;
c) filtering said pixel data to remove noise;
d) computing a local discriminating feature, selected from the group consisting of standard deviation and spread, to identify a text region in said image, wherein a region is identified as text when said feature is above a local feature threshold value;
e) analyzing a luminance histogram of said image to identify a background region in said image, wherein a region is identified as background when an initial maximum histogram bin containing the highest number of pixels exceeds a background threshold value;
f) verifying said background region analysis using region chrominance data;
g) labeling any background regions as such;
h) analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
i) analyzing said contone regions to identify text regions present within said contone regions;
j) analyzing said contone regions to identify background regions present in said contone regions;
k) analyzing areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions;
l) repeating steps e-g until no further sub-regions are found; and
m) analyzing said contone regions and said contone sub-regions to identify pictorial contone regions and non-pictorial contone regions. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. An image segmentation apparatus, said apparatus comprising:
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a) a reader for obtaining pixel attribute data for a mixed-content image;
b) a text identifier for identifying a text region in said image;
c) a background identifier for identifying a background region in said image;
d) a contone analyzer for analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
e) wherein said text analyzer may analyze said contone regions to identify any text regions present within said contone regions;
f) wherein said background analyzer may analyze said contone regions to identify any background regions present in said contone regions;
g) wherein said contone analyzer may analyze areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions; and
h) wherein said text analyzer, said background analyzer and said contone analyzer may operate recursively on regions and sub-regions to identify nested regional attributes.
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26. A computer-readable medium comprising instructions for:
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a) obtaining pixel attribute data for a mixed-content image;
b) identifying a text region in said image;
c) identifying a background region in said image;
d) analyzing areas in said image outside any of said background regions and outside any of said text regions to identify contone regions;
e) analyzing said contone regions to identify any text regions present within said contone regions;
f) analyzing said contone regions to identify any background regions present in said contone regions;
g) analyzing areas in said contone regions outside any of said background regions and outside any of said text regions to identify contone sub-regions; and
h) repeating steps e-g until no further sub-regions are found.
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Specification