Apparatus and method for detecting character region in image

• US 20070116360A1
• Filed: 11/09/2006
• Published: 05/24/2007
• Est. Priority Date: 11/21/2005
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Moon, Youngsu, Kim, Jiyeun, Jung, Cheolkon, Feng, Lui
• Application Number:
  US11/594,827
• Publication Number:
  US 20070116360A1
• Time in Patent Office: Days
• US Class Current: 382/176
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/147 Determination of region of interest
• CPC Class Codes: G06V 30/18076 by analysing connectivity, e.g. edge linking, connected component analysis or slices

Abstract

An apparatus and method for detecting a character region in an image. The apparatus includes a character candidate region detecting unit which detects a character candidate region from the image by detecting character strokes; and a character region checking unit which checks whether the detected character candidate region is the character region in response to the detected result of the character candidate region detecting unit.

Claims

1. An apparatus for detecting a character region in an image, comprising:

a character candidate region detecting unit which detects a character candidate region from the image by detecting character strokes; and

a character region checking unit which checks whether the detected character candidate region is the character region in response to the detected result of the character candidate region detecting unit.

2. The apparatus of claim 1, wherein the character candidate region detecting unit comprises:

a character stroke detecting unit which detects the character strokes from the image;

a connection element analyzing unit which analyzes connection elements for each character stroke region of the detected character strokes and readjusts the character stroke regions; and

a candidate region determining unit which determines the character candidate region by orthogonally projecting pixels of the readjusted character stroke regions in vertical and horizontal directions.

3. The apparatus of claim 2, wherein the character stroke detecting unit detects the character strokes using a character stroke filter while scanning the image.

4. The apparatus of claim 3, wherein the character stroke detecting unit detects the character strokes while varying the angle of the character stroke filter.

5. The apparatus of claim 3, wherein the character stroke detecting unit detects the character strokes while varying the size of the character stroke filter.

6. The apparatus of claim 3, wherein the character stroke filter comprises:

a first filter;

a second filter; and

a third filter,

wherein each of the first, second and third filters have a rectangular shape.

7. The apparatus of claim 6, wherein the character stroke detecting unit detects as the character strokes a region in which a filtering value obtained by

$\begin{array}{c}{R}_1\end{array}$

( α

, d ) = 1 m 1 ( 2 ) ⁡

[ 

m 1 ( 1 ) - m 2 ( 1 ) 

+ 

m 1 ( 1 ) - m 3 ( 1 ) 

- 

m 2 ( 1 ) - m 3 ( 1 ) 

]  

exceeds a first threshold value,

where, R(α

, d) is the filtering value, α

is an angle of the character stroke filter, d is the vertical width of the first filter, m₁⁽¹⁾ is an average of the values of the pixels included in the first filter, m₂⁽¹⁾ is an average of the values of the pixels included in the second filter, m₃⁽¹⁾ is an average of the values of the pixels included in the third filter, and m₁⁽²⁾ is a variance of the values of the pixels included in the first filter.

8. The apparatus of claim 2, wherein the connection element analyzing unit unifies adjacent character stroke regions into one character stroke region when a plurality of character stroke regions are adjacent to one another at the upper, lower, left, and right sides thereof.

9. The apparatus of claim 2, wherein the connection element analyzing unit excludes the character stroke region from the character candidate region, if pixel number of the character stroke region is less than a predetermined number.

10. The apparatus of claim 2, wherein the candidate region determining unit determines the character stroke region which histogram results by orthogonally projecting the pixels of the character stroke region in the horizontal direction and the vertical direction exceed a first comparative value and a second comparative value as the character candidate region.

11. The apparatus of claim 2, wherein the character candidate region detecting unit further comprises an edge detecting unit which detects an edge from the image.

12. The apparatus of claim 11, wherein the character candidate region detecting unit further comprises a first morphology processing unit which performs a morphology process on the detected edge.

13. The apparatus of claim 2, wherein the character candidate region detecting unit further comprises a second morphology processing unit which performs a morphology process on the detected character strokes.

14. The apparatus of claim 1, wherein the character region checking unit comprises:

a feature value detecting unit which detects normalized intensity feature values and constant gradient variance (CGV) feature values of partial regions obtained by dividing the detected character candidate region by a predetermined size; and

a first score calculating unit which unifies the normalized intensity feature values and the CGV feature values of the partial regions and calculates character region determining scores of the partial regions; and

a character region determining unit which compares an average of the calculated character region determining scores with a second threshold value and determines the character candidate region to the character region according to the compared result.

15. The apparatus of claim 14, wherein the feature value detecting unit comprises:

a candidate region size adjusting unit which adjusts the size of the detected character candidate region;

a partial region detecting unit which detects the partial regions of the character candidate region having the adjusted size using a window having a predetermined size;

a normalized intensity feature value detecting unit which detects the normalized intensity feature values of the detected partial regions; and

a CGV feature value detecting unit which detects the CGV feature values of the detected partial regions.

16. The apparatus of claim 15, wherein the normalized intensity feature value detecting unit detects normalized intensity feature value components of the pixels of any partial region using

Nf(s)=(f(s)−

V_min)/(V_max−

V_min)*L

where, Nf(s) is the normalized intensity feature value component of the pixel s in any partial region, f(s) is the intensity value of the pixel s, V_min denotes a lowest intensity value among the intensity values of the pixels in any partial region, V_max is a highest intensity value among the intensity values of the pixels in any partial region, and L is a constant for normalizing the intensity value.

17. The apparatus of claim 15, wherein the CGV feature value detecting unit detects the CGV feature value components of the pixels of any partial region using

$\mathrm{CGV}$

( s ) = ( g ⁡

( s ) - LM ⁡

( s ) ) ⁢

GV LV ⁡

( s )

where, CGV(s) is the CGV feature value component of the pixel s in any partial region, g(s) is the gradient size of the pixel s, LM(s) is an average of the intensity values of the pixels in a predetermined range from the pixel s, LV(s) is a variance of the intensity values of the pixels in the predetermined range from the pixel s, and GV is a variance of the intensity values of the pixels in any partial region.

18. The apparatus of claim 14, wherein the first score calculating unit calculates the character region determining score of any partial region using

F ₀ =P ₁ F ₁ +P ₂ F ₂

where, F₀ is the character region determining score of any partial region, F₁ is an output score of support vector machine (SVM) of the normalized intensity feature value of any partial region, F₂ is an output score of support vector machine (SVM) of the CGV feature value of any partial region, P₁ is a pre-trained prior probability of the normalized intensity feature value, and P₂ is a pre-trained prior probability of the CGV feature value.

19. The apparatus of claim 1, further comprising:

an image size adjusting unit which adjusts the size of the image; and

a detected result combining unit which selects an image having the largest average from the averages of the character region determining scores of the same character region detected from the images having the adjusted sizes.

20. The apparatus of claim 1, further comprising a boundary correcting unit which corrects the boundary of the detected character region.

21. The apparatus of claim 20, wherein the boundary correcting unit comprises a boundary line reducing unit which checks whether the character region determining scores of partial regions obtained by dividing the detected character region by a predetermined size are less than a third threshold value and reduces the boundary line of the character region according to the checked result.

22. The apparatus of claim 20, wherein the boundary correcting unit comprises a boundary line coupling unit which checks an interval between the detected character regions and couples the boundary lines of the detected character regions.

23. The apparatus of claim 22, wherein the boundary line coupling unit comprises:

an interval checking unit which checks an interval between the detected character regions;

a second score calculating unit which calculates the character region determining scores of the partial regions obtained by dividing a region between the character regions by a predetermined size according to the checked result of the interval checking unit; and

a coupling unit which compares an average of the calculated character region determining scores with a fourth threshold value and couples the boundary lines of the detected character regions according to the compared result.

24. The apparatus of claim 20, wherein the boundary correcting unit comprises a boundary line expanding unit which detects a similarity in pixel distribution between the detected character region and a center region of the detected character region and expands the boundary line of the detected character region according to the detected similarity.

25. The apparatus of claim 24, wherein the boundary line expanding unit expands the boundary line of the detected character region when the detected similarity exceeds a predetermined reference value and an average of the character region determining scores of the partial regions exceeds a fifth threshold value.

26. A method of detecting a character region in an image, comprising:

detecting a character candidate region from the image by detecting character strokes; and

checking whether the detected character candidate region is the character region.

27. The method of claim 26, wherein the detecting of the character candidate region comprises:

detecting the character strokes from the image;

analyzing connection elements for each character stroke region of the detected character strokes and readjusting the character stroke regions; and

determining the character candidate region by orthogonally projecting pixels of the readjusted character stroke regions in vertical and horizontal directions.

28. The method of claim 27, wherein, in the detecting of the character strokes, the character strokes are detected using a character stroke filter while scanning the image.

29. The method of claim 28, wherein, in the detecting of the character strokes, the character strokes are detected while varying the angle of the character stroke filter.

30. The method of claim 28, wherein, in the detecting of the character strokes, the character strokes are detected while varying the size of the character stroke filter.

31. The method of claim 28, wherein the character stroke filter comprises:

a first filter;

a second filter; and

a third filter,

wherein each of the first, second and third filters have a rectangular shape.

32. The method of claim 31, wherein, in the detecting of the character strokes, a region in which a filtering value obtained by

$R_1$

( α

, d ) = 1 m 1 ( 2 ) ⁡

[ 

m 1 ( 1 ) - m 2 ( 1 ) 

+ 

m 1 ( 1 ) - m 3 ( 1 ) 

- 

m 2 ( 1 ) - m 3 ( 1 ) 

]

exceeds a first threshold value is detected as the character strokes,

where, R(α

, d) is the filtering value, α

denotes an angle of the character stroke filter, d is the vertical width of the first filter, m₁⁽¹⁾ is an average of the values of the pixels included in the first filter, m₂⁽¹⁾ is an average of the values of the pixels included in the second filter, m₃⁽¹⁾ is an average of the values of the pixels included in the third filter, and m₁⁽²⁾ is a variance of the values of the pixels included in the first filter.

33. The method of claim 27, wherein, in the analyzing of the connection elements, adjacent character stroke regions are unified into one character stroke region when a plurality of character stroke regions are adjacent to one another at the upper, lower, left, and right sides thereof.

34. The method of claim 27, wherein in the analyzing of the connection elements, the character stroke region is excluded from the character candidate region, if pixel number of the character stroke region is less than a predetermined number.

35. The method of claim 27, wherein, in the determining of the character candidate region, determining the character stroke region which histogram results by orthogonally projecting the pixels of the character stroke region in the horizontal direction and the vertical direction exceed a first comparative value and a second comparative value as the character candidate region.

36. The method of claim 27, wherein the detecting of the character candidate region further comprises detecting an edge from the image,

wherein the detecting of the character strokes is performed after the detecting the edge.

37. The method of claim 36, wherein the detecting of the character candidate region further comprises performing a morphology process on the detected edge,

wherein the detecting of the character strokes is performed after performing the morphology process.

38. The method of claim 27, wherein the detecting of the character candidate region further comprises performing a morphology process on the detected character strokes,

wherein the detecting of the character strokes is performed after the performing the morphology process.

39. The method of claim 26, wherein the checking of whether the detected character candidate region is the character region comprises:

detecting normalized intensity feature values and constant gradient variance (CGV) feature values of partial regions obtained by dividing the detected character candidate region by a predetermined size; and

unifying the normalized intensity feature values and the CGV feature values of the partial regions and calculating character region determining scores of the partial regions; and

comparing an average of the calculated character region determining scores with a second threshold value and determining the character candidate region to the character region according to the compared result.

40. The method of claim 39, wherein the detecting of the feature values comprises:

adjusting the size of the detected character candidate region;

detecting the partial regions of the character candidate region having the adjusted size using a window having a predetermined size;

detecting the normalized intensity feature values and the CGV feature values of the detected partial regions.

41. The method of claim 40, wherein, in the detecting of the normalized intensity feature values and the CGV feature values, normalized intensity feature value components of the pixels of any partial region are detected using

Nf(s)=(f(s)−

V_min)/(V_max−

V_min)*L

where, Nf(s) is the normalized intensity feature value component of the pixel s in any partial region, f(s) is the intensity value of the pixel s, V_min is a lowest intensity value among the intensity values of the pixels in any partial region, V_max is a highest intensity value among the intensity values of the pixels in any partial region, and L is a constant for normalizing the intensity value.

42. The method of claim 40, wherein, in the detecting of the normalized intensity feature values and the CGV feature values the CGV feature value components of the pixels of any partial region are detected using

$\mathrm{CGV}$

( s ) = ( g ⁡

( s ) - LM ⁡

( s ) ) ⁢

GV LV ⁡

( s )

where, CGV(s) is the CGV feature value component of the pixel s in any partial region, g(s) denotes the gradient size of the pixel s, LM(s) is an average of the intensity values of the pixels in a predetermined range from the pixel s, LV(s) is a variance of the intensity values of the pixels in the predetermined range from the pixel s, and GV is a variance of the intensity values of the pixels in any partial region.

43. The method of claim 39, wherein, in the unifying of the normalized intensity feature values and the CGV feature values, the character region determining score of any partial region is calculated using

F ₀ =P ₁ F ₁ +P ₂ F ₂

where, F₀ is the character region determining score of any partial region, F₁ is an output score of support vector machine (SVM) of the normalized intensity feature value of any partial region, F₂ is an output score of support vector machine (SVM) of the CGV feature value of any partial region, P₁ is a pre-trained prior probability of the normalized intensity feature value, and P₂ is a pre-trained prior probability of the CGV feature value.

44. The method of claim 26, further comprising:

adjusting the size of the image; and

selecting an image having the largest average from the averages of the character region determining scores of the same character region detected from the images having the adjusted sizes.

45. The method of claim 26, further comprising correcting the boundary of the detected character region.

46. The method of claim 45, wherein the correcting of the boundary comprises checking whether the character region determining scores of partial regions obtained by dividing the detected character region by a predetermined size are less than a third threshold value and reducing the boundary line of the character region according to the checked result.

47. The method of claim 45, wherein the correcting of the boundary comprises checking an interval between the detected character regions and coupling the boundary lines of the detected character regions.

48. The method of claim 47, wherein the checking of the interval comprises:

checking an interval between the detected character regions;

calculating the character region determining scores of the partial regions obtained by dividing a region between the character regions by a predetermined size; and

comparing an average of the calculated character region determining scores with a fourth threshold value and coupling the boundary lines of the detected character regions according to the compared result.

49. The method of claim 45, wherein the correcting of the boundary comprises detecting a similarity in pixel distribution between the detected character region and a center region of the detected character region and expanding the boundary line of the detected character region according to the detected similarity.

50. The method of claim 49, wherein, in the detecting of the similarity, the boundary line of the detected character region expands when the detected similarity exceeds a predetermined reference value and an average of the character region determining scores of the partial regions exceeds a fifth threshold value.

51. A computer-readable medium having embodied thereon a computer program for performing the method of claim 26.