COMPRESSION OF DIGITAL IMAGES OF SCANNED DOCUMENTS

• US 20120063686A1
• Filed: 11/17/2011
• Published: 03/15/2012
• Est. Priority Date: 05/04/2007
• Status: Active Grant

• Current Assignee: I.R.I.S. (Fidelity National Information Services Incorporated)
• Original Assignee: I.R.I.S. (Fidelity National Information Services Incorporated)
• Inventors: DAUW, Michel, DEMUELENAERE, Pierre
• Granted Patent: US 8,666,185 B2
• Time in Patent Office: Days
• US Class Current: 382/200
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 9/00 Image coding bandwidth or redundancy reduction for static pictures H04N1/41; coding or decoding of static colour picture signals H04N1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00
• CPC Class Codes: G06V 10/761 Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/148 Segmentation of character regions
• CPC Class Codes: G06V 30/162 Quantising the image signal
• CPC Class Codes: G06V 30/1902 Shifting or otherwise transforming the patterns to accommodate for positional errors
• CPC Class Codes: G06V 30/413 Classification of content, e.g. text, photographs or tables
• CPC Class Codes: H04N 1/40062 Discrimination between different image types, e.g. two-tone, continuous tone
• CPC Class Codes: H04N 1/41 Bandwidth or redundancy reduction by scanning H04N1/17 ; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00
• CPC Class Codes: H04N 19/21 with binary alpha-plane coding for video objects, e.g. context-based arithmetic encoding [CAE]
• CPC Class Codes: H04N 19/29 involving scalability at the object level, e.g. video object layer [VOL]
• CPC Class Codes: H04N 19/59 involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
• CPC Class Codes: H04N 19/85 using pre-processing or post-processing specially adapted for video compression

Abstract

A first aspect of the invention relates to a method for creating a binary mask image from an a inputted digital image of a scanned document, comprising the steps of creating a binarized image by binarizing the inputted digital image, detecting first text regions representing light text on a dark background, and inverting the first text regions, such that the inverted first text regions are interpretable in the same way as dark text on a light background. A second aspect of the invention relates to a method for comparing in a binary image a first pixel blob with a second pixel blob to determine whether they represent matching symbols, comprising the steps of detecting a line in one blob not present in the other and/or determining if one of the blobs represents an italicized symbol where the other does not.

Claims

1. A method for creating a binary mask image from an inputted digital image of a scanned document, comprising the steps of:

a) creating a binarized image by binarizing said inputted digital image,

b) detecting in said binarized image first text regions representing light text on a dark background in said inputted digital image,

c) inverting said first text regions in said binarized image, such that a transformed binary image is formed in which the inverted first text regions are interpretable in the same way as dark text on a light background,

wherein the steps of detecting and inverting the first text regions are performed on the basis of pixel blobs contained in the binarized image without interpreting the represented text.

2. The method according to claim 1, wherein the creation of said binary mask image further comprises the steps of:

d) detecting in said binarized image second text regions representing dark text on a light background in said inputted digital image.

e) eliminating from the binarized image text regions that represent no actual text.

3. The method according to claim 2, further comprising the steps of separating off horizontal and vertical graphical elements before said steps of detecting first and second text regions, and reintroducing the said horizontal and vertical graphical elements into the binarized image after said detection steps.

4. The method according to claim 1, wherein step a) comprises the following steps:

a1) building a grayscale image from said inputted digital image,

a2) detecting edges in said grayscale image, thereby building an edge binary image containing edge pixels and non-edge pixels,

a3) determining threshold values for each of said edge pixels on the basis of surrounding pixels and giving said non-edge pixels a null threshold value, thereby building a threshold grayscale image,

a4) determining threshold values for each of said non-edge pixels touching the edge pixels on the basis of surrounding threshold values,

a5) scaling said threshold grayscale image by keeping the maximum threshold values,

a6) propagating the threshold values from pixels having a positive value to pixels having a null value,

a7) building a first binary image on the basis of said grayscale image and said scaled threshold grayscale image.

5. The method according to claim 4, wherein step a2) involves the use of a canny edge algorithm for said edge detection.

6. The method according to claim 4, wherein step a) further comprises the following steps:

a8) building a second binary image on the basis of said grayscale image and said threshold grayscale image,

a9) building said binarized image by combining said first and second binary images.

7. The method according to claim 1, wherein said inputted digital image has a given resolution and said creation of said binary mask image involves reducing said resolution by a binary mask resolution reduction factor.

8. A method for text recognition, comprising the steps of:

creating a binary image from an inputted digital image of a scanned document by means of the following steps;

a. creating a binarized image by binarizing said inputted digital image;

b. detecting in said binarized image first text regions representing light text on a dark background in said inputted digital image;

c. detecting in said binarized image second text regions representing dark text on a light background in said inputted digital image; and

d. inverting said first text regions in said binarized image, such that a transformed binary image is formed in which the inverted first text regions are interpretable in the same way as the second text regions;

wherein the steps of detecting and inverting the first text regions are performed on the basis of pixel blobs contained in the binarized image without interpreting the represented text; and

applying a text recognition technique in which the inverted first text regions are recognized along with the second text regions.

9. A compression method for compressing an inputted digital image of a scanned document, said compression method comprising the steps of:

a) segmenting said inputted digital image into multiple image layers comprising a foreground image containing color information for foreground elements of said document, a background image containing color information for background elements of said document and a binary mask image for selecting between pixels in said foreground image and said background image upon decompressing said compressed digital image, and

b) compressing each of the image layers by means of a suitable compression technique, thereby obtaining a compressed digital image,

wherein creating the binary mask image involves the steps of;

creating a binarized image by binarizing said inputted digital image,

detecting in said binarized image first text regions representing light text on a dark background in said inputted digital image,

inverting said first text regions in said binarized image, such that a transformed binary image is formed in which the inverted first text regions are interpretable in the same way as dark text on a light background,

wherein the steps of detecting and inverting the first text regions are performed on the basis of pixel blobs contained in the binarized image without interpreting the represented text.

10. The method according to claim 9, wherein said inputted digital image has a given resolution and said creation of said binary mask image involves reducing said resolution by a binary mask resolution reduction factor.

11. The method according to claim 9, wherein said inputted digital image has a given resolution and said foreground and background images are built by reducing said resolution by respectively a foreground resolution reduction factor and a background resolution reduction factor.

12. The method according to claim 9, wherein step b) comprises the steps of:

b1) compressing said foreground and background images by means of an image compression technique,

b2) compressing said binary mask image by means of a symbol-based compression technique.

13. A computer program product directly loadable into a memory of a computer, comprising software code portions for performing the following steps when said product is run on a computer:

a) segmenting an inputted digital image of a scanned document into multiple image layers comprising a foreground image containing color information for foreground elements of said document, a background image containing color information for background elements of said document and a binary mask image for selecting between pixels in said foreground image and said background image upon decompressing said compressed digital image, and

b) compressing each of the image layers by means of a suitable compression technique, thereby obtaining a compressed digital image,

wherein creating the binary mask image involves the steps of;

creating a binarized image by binarizing said inputted digital image,

detecting in said binarized image first text regions representing light text on a dark background in said inputted digital image,

inverting said first text regions in said binarized image, such that a transformed binary image is formed in which the inverted first text regions are interpretable in the same way as dark text on a light background,

wherein the steps of detecting and inverting the first text regions are performed on the basis of pixel blobs contained in the binarized image without interpreting the represented text.

14. A computer program product according to claim 13, stored on a computer usable medium.