METHODS AND APPARATUS FOR TEXT DETECTION

• US 20070127815A1
• Filed: 02/12/2007
• Published: 06/07/2007
• Est. Priority Date: 03/14/2001
• Status: Abandoned Application

• Current Assignee: Electronics For Imaging Incorporated
• Original Assignee: Electronics For Imaging Incorporated
• Inventors: Karidi, Ron, Man, Lai
• Application Number:
  US11/674,116
• Publication Number:
  US 20070127815A1
• Time in Patent Office: Days
• US Class Current: 382/176
• CPC Class Codes: G06V 30/413 Classification of content, e.g. text, photographs or tables

Abstract

A text detection technique comprises local ramp detection, identification of intensity troughs (candidate text strokes), determination of stroke width, preliminary detection of text based on contrast and stroke width, and a consistency check.

Claims

1. A method having scanned intensity information as input for detecting text in a scanned page by observing a very strong contrast in a localized region between a dark side and a light side, the method comprising:

determining a stroke width;

contrast-based text detection processing;

wherein the localized region comprises a substantially sharp edge between the dark side and the light side; and

whereby any of black text on white background, black text on color background, and white or light text on a dark background are detected.

2. The method of claim 1, further comprising measuring a color saturation value and using the value to improve detection accuracy, wherein the color saturation value of the dark side is required to be small.

3. The method of claim 2, further comprising preliminarily single pixel processing to estimate the color saturation value using prior color information provided by the scanner.

4. The method of claim 1, furthering comprising detecting the presence of half-tone pixels by using a local indicator to improve detection accuracy.

5. The method of claim 4, wherein the half-tone detection is obtained through an algorithm for half-tone detection.

6. The method of claim 1, wherein the pre-processing further comprises:

detecting a local ramp; and

identifying an intensity trough.

7. The method of claim 1, wherein the contrast-based text detection processing further comprises:

detecting text preliminarily based on local contrast and stroke width; and

consistency checking.

8. The method of claim 1, wherein the observing a strong contrast further comprises:

detecting text preliminarily based on local contrast; and

consistency checking.

9. The method of claim 6, further comprising:

detecting a local ramp;

identifying an intensity trough;

detecting text preliminarily based on contrast and stroke width; and

consistency checking.

10. The method of claim 6, wherein identifying an intensity trough uses a finite state machine algorithm, the algorithm having a sweeping procedure.

11. The method of claim 6, wherein the stroke width determination step further comprises:

determining a width and a skeleton, wherein the width is a distance value and the skeleton is a skeletal line; and

detecting closely touching text strokes.

12. The method of claim 11, wherein the width and skeleton determining step further comprises:

setting the width value to the smaller of a vertical distance and a horizontal distance between two edges of the stroke; and

determining the skeletal line as a roughly equidistant line from the edges.

13. The method of claim 11, wherein the detecting closely touching text strokes further comprises detecting a pattern of dark-light-dark (DLD) in a horizontal or a vertical direction within a very small window.

14. The method of claim 7, wherein the detecting text further comprises deciding whether a current pixel is a text pixel by using the local contrast present in an N×

N window having a center over a set of pixels and centered at the current pixel, and stroke width at the current pixel.

15. The method of claim 14, wherein N=9.

16. The method of claim 14, wherein numerous statistics of the pixels within the N×

N window are collected by using a set of thresholds.

17. The method of claim 16, wherein

the set of thresholds comprises any of:

a first minimum intensity level for text background;

a maximum intensity level of text to be detected;

a second minimum intensity level for text background around crowded text strokes, wherein the second minimum intensity level is smaller than the first minimum intensity level;

a medium threshold value, wherein the medium threshold value is around 50% intensity;

a first maximum width of a stroke, wherein the first width is considered thin; and

a second maximum width of a stroke, wherein the second width is considered very thin; and

wherein the numerous statistics comprise any of;

a number of pixels that are thin;

a number of pixels in the center of a 3×

3 window that are thin;

a number of pixels on a skeleton, wherein the skeleton pixels are very thin;

a minimum width among pixels of the center 3×

3 pixels;

a second smallest width among the pixels of the center 3×

3 pixels, wherein the second smallest width is equal to the minimum width among pixels of the center 3×

3 pixels if more than 1 pixel has the minimum width;

a highest intensity present in the N×

N window;

a number of light pixels;

a number of non-light pixels;

a number of non-light pixels detected as half-toned from a half-tone detection module;

a number of dark and neutral pixels;

a number of dark and colored pixels;

a number of colored pixels with medium intensity;

a number of dark and neutral pixels after boosting;

a number of pixels in the center 3×

3 window, wherein the pixels are dark to medium in intensity;

a thin flag set to 1 if the stroke is thin, or set to zero otherwise; and

a background flag set to 1 if the center 3×

3 pixels are all light, or set to zero otherwise.

18. The method of claim 16, further comprising determining if the current pixel is in a category of a set of predetermined categories using an associated algorithm and the set of thresholds, wherein the thresholds are chosen empirically.

19. The method of claim 18, wherein the predetermined set of categories comprises:

Text Outline;

Text Body;

Background; and

Non-text.

20. The method of claim 18, further comprising moving the center of the N×

N window by J pixels to obtain a subsampled text tag.

21. The method of claim 20, wherein J=3.

22. The method of claim 7, wherein the consistency checking further comprises:

accumulating a set of statistics using an N×

N window of text tags and a set of thresholds; and

deciding by using the set of statistics if each of the text tags is any of;

Text Outline;

Text Body;

Background; and

Non-text.

23. The method of claim 22, wherein the N×

N window further comprises N×

N blocks, each block representing J×

J pixels.

24. The method of claim 23, wherein N=5 and J=3.

25. The method of claim 22, wherein set the of thresholds comprises a maximum number of Non-text blocks threshold.

26. An apparatus for receiving scanned intensity information as input for detecting text in a scanned page by observing a very strong contrast in a localized region between a dark side and a light side, the apparatus comprising:

a module for pre-processing for stroke width determination; and

a module for contrast-based text detection processing;

wherein the localized region comprises a substantially sharp edge between the dark side and the light side; and

whereby any of black text on white background, black text on color background, and white or light text on a dark background are detected.