METHOD FOR BINARIZING SCANNED DOCUMENT IMAGES CONTAINING GRAY OR LIGHT COLORED TEXT PRINTED WITH HALFTONE PATTERN

• US 20120120453A1
• Filed: 11/15/2010
• Published: 05/17/2012
• Est. Priority Date: 11/15/2010
• Status: Active Grant

• Current Assignee: Konica Minolta Laboratory U.S.A., Inc. (Konica Minolta Inc.)
• Original Assignee: Konica Minolta Systems Lab Incorporated (Konica Minolta Inc.)
• Inventors: MING, Wei, YU, Songyang
• Granted Patent: US 8,947,736 B2
• Time in Patent Office: Days
• US Class Current: 358/3.06
• CPC Class Codes: H04N 1/40062 Discrimination between different image types, e.g. two-tone, continuous tone
• CPC Class Codes: H04N 1/405 Halftoning, i.e. converting the picture signal of a continuous-tone original into a corresponding signal showing only two levels

Abstract

A method for binarizing a scanned document images containing gray or light colored text printed with halftone patterns. The document image is initially binarized and connected image components are extracted from the initial binary image as text characters. Each text character is classified as either a halftone text character or a non-halftone text character based on an analysis of its topology features. The topology features may be the Euler number of the text character; a text character with a Euler number below −2 is classified as halftone text. The gray-scale document image is then divided into halftone text regions containing only halftone text characters and non-halftone text regions. Each region is binarized using its own pixel value statistics. This eliminates the influence of black text on the threshold values for binarizing halftone text. The binary maps of the regions are combined to generate the final binary map.

Claims

1. A method implemented in a data processing apparatus for binarizing a gray-scale document image which has been generated by scanning a paper-based document, the method comprising:

(a) identifying text characters in the gray-scale document image;

(b) classifying each text character identified in step (a) as either a halftone text character or a non-halftone text character based on a topological analysis of the text character; and

(c) binarizing halftone text characters using pixel value characteristics obtained from only halftone text characters classified in step (b).

2. The method of claim 1, further comprising:

(d) after step (b) and before step (c), dividing the gray-scale document image into halftone text regions containing only halftone text characters and non-halftone text regions containing non-halftone text characters, wherein step (c) comprises binarizing each halftone text region using pixel value statistics calculated from pixels in that region only, to generate a binary map for each halftone text region.

3. The method of claim 2, further comprising:

(e) after step (d), binarizing each non-halftone text region using pixel value statistics calculated from pixels in that region only, to generate a binary map for each non-halftone text region.

4. The method of claim 3, further comprising:

(f) combining the binary maps for the halftone text regions and the binary maps for the non-halftone text region generated in steps (c) and (e) to generate a binary map of the gray-scale document image.

5. The method of claim 1, wherein step (a) comprises:

performing an initial binarization of the gray-scale image to generate an initial binary image; and

extracting connected image components in the initial binary image as text characters.

6. The method of claim 5, wherein step (b) comprises:

calculating an Euler number for each text character; and

classifying a text character as halftone text if the Euler number for the text character is below a predetermined value, and classifying a text character as non-halftone text if the Euler number of the text character is equal to or above the predetermined value.

7. The method of claim 2, wherein step (d) comprises applying binary morphological operations to all non-halftone text characters or to all halftone text characters or to both.

8. The method of claim 2, wherein step (c) comprises applying a local thresholding method to each halftone text region.

9. The method of claim 3, wherein step (e) comprises applying a local thresholding method or a global thresholding method to each non-halftone text region.

10. The method of claim 1, wherein step (c) comprises:

(c1) calculating pixel value statistics from at least some of the halftone text characters classified in step (b), the pixel value statistics including a mean pixel value, a minimum pixel value and a maximum pixel value; and

(c2) binarizing the document image using the pixel value statistics calculated in step (c1) to generate a first binary image;

wherein the method further comprises;

binarizing the document image to generate a second binary image; and

combining the first and second binary image using a bitwise AND operation.

11. A computer program product comprising a computer usable non-transitory medium having a computer readable program code embedded therein for controlling a data processing apparatus, the computer readable program code configured to cause the data processing apparatus to execute a process for binarizing a gray-scale document image which has been generated by scanning a paper-based document, the process comprising:

(a) identifying text characters in the gray-scale document image;

(b) classifying each text character identified in step (a) as either a halftone text character or a non-halftone text character based on a topological analysis of the text character; and

(c) binarizing halftone text characters using pixel value characteristics obtained from only halftone text characters classified in step (b).

12. The computer program product of claim 11, wherein the process further comprises:

(d) after step (b) and before step (c), dividing the gray-scale document image into halftone text regions containing only halftone text characters and non-halftone text regions containing non-halftone text characters;

wherein step (c) comprises binarizing each halftone text region using pixel value statistics calculated from pixels in that region only, to generate a binary map for each halftone text region.

13. The computer program product of claim 12, wherein the process further comprises:

(e) after step (d), binarizing each non-halftone text region using pixel value statistics calculated from pixels in that region only, to generate a binary map for each non-halftone text region.

14. The computer program product of claim 13, wherein the process further comprises:

(f) combining the binary maps for the halftone text regions and the binary maps for the non-halftone text region generated in steps (c) and (e) to generate a binary map of the gray-scale document image.

15. The computer program product of claim 11, wherein step (a) comprises:

performing an initial binarization of the gray-scale image to generate an initial binary image; and

extracting connected image components in the initial binary image as text characters.

16. The computer program product of claim 15, wherein step (b) comprises:

calculating an Euler number for each text character; and

classifying a text character as halftone text if the Euler number for the text character is below a predetermined value, and classifying a text character as non-halftone text if the Euler number of the text character is equal to or above the predetermined value.

17. The computer program product of claim 11, wherein step (d) comprises applying binary morphological operations to all non-halftone text characters or to all halftone text characters or to both.

18. The computer program product of claim 12, wherein step (c) comprises applying a local thresholding method to each halftone text region.

19. The computer program product of claim 13, wherein step (e) comprises applying a local thresholding method or a global thresholding method to each non-halftone text region.

20. The computer program product of claim 11, wherein step (c) comprises:

(c1) calculating pixel value statistics from at least some of the halftone text characters classified in step (b), the pixel value statistics including a mean pixel value, a minimum pixel value and a maximum pixel value; and

(c2) binarizing the document image using the pixel value statistics calculated in step (c1) to generate a first binary image;

wherein the process further comprises;

binarizing the document image to generate a second binary image; and

combining the first and second binary image using a bitwise AND operation.

21. A scanner comprising:

a scanning section for scanning a hard copy document to generate a gray-scale document image; and

a data processing apparatus for processing the gray-scale document image to generate a binary map of the gray-scale document image,

wherein the processing of the gray-scale document image includes;

(a) identifying text characters in the gray-scale document image, (b) classifying each text character identified in step (a) as either a halftone text character or a non-halftone text character based on a topological analysis of the text character, and (c) binarizing halftone text characters using pixel value characteristics obtained from only halftone text characters classified in step (b).

22. The scanner of claim 21, wherein the processing further includes:

(d) after step (b) and before step (c), dividing the gray-scale document image into halftone text regions containing only halftone text characters and non-halftone text regions containing non-halftone text characters, wherein step (c) comprises binarizing each halftone text region using pixel value statistics calculated from pixels in that region only, to generate a binary map for each halftone text region, (e) after step (d), binarizing each non-halftone text region using pixel value statistics calculated from pixels in that region only, to generate a binary map for each non-halftone text region, and (f) combining the binary maps for the halftone text regions and the binary maps for the non-halftone text region generated in steps (c) and (e) to generate the binary map.

23. The scanner of claim 21, wherein step (a) comprises:

performing an initial binarization of the gray-scale image to generate an initial binary image; and

extracting connected image components in the initial binary image as text characters.

24. The scanner of claim 23, wherein step (b) comprises:

calculating an Euler number for each text character; and

classifying a text character as halftone text if the Euler number for the text character is below a predetermined value, and classifying a text character as non-halftone text if the Euler number of the text character is equal to or above the predetermined value.

25. The scanner of claim 21, wherein step (d) comprises applying binary morphological operations to all non-halftone text characters or to all halftone text characters or to both.

26. The scanner of claim 21, wherein step (c) comprises applying a local thresholding method to each halftone text region.

27. The scanner of claim 21, wherein step (e) comprises applying a local thresholding method or a global thresholding method to each non-halftone text region.