ISBN: 378-26-138420-01
INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN ENGINEERING RESEARCH, ICCTER - 2014
A REVIEW ON ENHANCEMENT OF DEGRADED DOCUMENT IMAGES BASED ON IMAGE BINARIZATION SYSTEM 1
2
N.S.Bharath Kumar.
Sreenivasan.B,
3
A.Rajani
M.Tech (DSCE) Student,
Assistant professor Assistant professor Department of ECE,AITS Annamacharya Institute of Technology and Sciences,Tirupati,India-517520 1
nsnandi1990@gmail.com 2 srinu3215@gmail.com 3 rajanirevanth446@gmail.com
the document text and background. Thresholding plays major role in Binarization. Thresholding is mainly dived into two classes’ namely global thresholding and local thresholding. The major issues that appear in the degraded document images are variation in the character stroke width, stroke brightness, stroke connections and shadows on image objects, smear and strains. In some handwritten degraded document images the ink of one side has disposed into the other side. A particular and efficient technique has used to achieve best results. Image Binarization technique was become popular due to its step-wise procedure to reduce the normalization noise and other issues that appears in the previous methods and finally follows a post processing procedure to yield good results. Image Binarization technique extends the previous local maximum and minimum method and the methods that are used in the latest Document Image Binarization Contests. Binarization Technique having the capability of handling many degraded documents with minimum parameter tuning. Historical documents will not have a clear bimodal pattern, so global thresholding is not a suitable approach for document Binarization. Global thresholding is simple, but cannot work properly for complex document backgrounds. Adaptive or Local thresholding which estimates thresholds for each and every pixel in an image is usually a better approach for historical document Binarization. Image Binarization Technique dose not uses any windowing techniques as in the case of proposed technique is, it existing methods. Binarization Technique calculates the image contrast by using local maximum and minimum. When compared with image gradient, image contrast is more responsible for detecting high contrast image pixels that was present around the text stroke boundaries. Thresholding method is capable of handling complex variations in the background such as uneven illumination, uneven surface, noise and bleeds through. Adaptive Contrast image makes use of image gradient widely for edge detection and to find out the text stroke edges. The existing method for the degraded document images is Ni-blacks approach “An Introduction to the Digital Image processing”. The procedure that was involved in the Ni-black approach to separate text from background is de-noising through Weiner filter, estimation of the foreground regions, estimation of the background
Abstract — Goal of separating the text from low quality and noisy document images are big challenging task due to small or large variation between the foreground and background regions of the various document images. The new document Image Binarization Technique is used to addresses these issues by using Adaptive Image Contrast. The Adaptive Image Contrast is the combination of local image contrast and the local image Gradient that is equivalent to text and background variations caused by different degraded document images. Image Binarization Technique involves the process of constructing adaptive contrast map for the input degraded image. The contrast map is then binarized and is combined with the Cannie’s edge map to determine the text stroke edge pixels. Edge detection through the canny process will gives accurate results instead of producing responses for the non edges. The Cannie’s process involves the process of reducing the amount unwanted and error information through the filtering process. The next step is the separation of text from document by adaptive thresholding that is determined based on the intensities of the detected text stroke edge pixels. Document Image Binarization Technique is simple, robust and yields good results when compared with several different database sets that are evaluated by different techniques.
Keywords: Adaptive Contrast Image, Document Analysis, Degraded Document Binarization Process, Edge Detection, Local Thresholding.
I.INTRODUCTION The Document Image Binarization technique is applied at the starting stage of document analysis process. Binarization refers to the process of converting the gray scale image into a binary image. Binarization Technique segments the image pixels into two fields namely white pixels as foreground text and black as the background surface of the document. A fast and accurate Image Binarization technique was chosen in order to accomplish the document analysis task such as Optical Character Recognition (OCR).As more and more degraded documents are digitized, the importance of such document Image Binarization also increases accordingly. Thresholding of degraded document images is an unsolved problem due to high and little variations between
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surface, interpolating the foreground pixels with the background surface, finally post processing is required. Niblack approach calculates the local threshold by using the mean and standard variation of image pixels within the local neighborhood window. The main disadvantage of the window based thresholding techniques is the thresholding performance depends on window size and character stroke width. Ni-blacks approach introduces some extra noise during the detection of the Pixels on the results of approximated foreground region. The local image contrast and local image gradient are also plays major role in the segmentation of the text from document background. Local image contrast and image gradient are very effective and have been used in many image Binarization techniques. II.OVERVIEW
Minimum (LMM) method will have a good property that it is tolerant to uneven illumination. The LMM method first constructs a contrast image and detects high contrast image pixels that exist around text stroke boundary. Thresholding is applied in order to separate the text and finally post processing is applied. The LMM technique yields a good result when it is applied to a datasets that are used in the recent Document image Binarization Contests (DIBCO). Thresholding of degraded document images is a serious problem due to large variations between the foreground and background as shown in the below figure.
2.1 DOCUMENT IMAGE BINARIZATION SYSTEM In order to overcome the issues appears in the degraded document images there is a necessity to develop an enhanced system for low contrast and noisy document images. Document Image Binarization scheme involved in the Document Image Binarization Contest (IBCO) for degraded document images is an effective scheme for analysis of text from degraded documents. Binarization technique is a well suitable procedure for the applications like Optical Character Recognition (OCR), Contrast Image Enhancement (CIE). The recent Document Image Binarization Contest (IBCO) held under the combined framework of International Conference on Document Analysis and Recognition (IDAR) and Handwritten Document Image Binarization Contest (HDIBC) shows recent efforts on these issues. The adaptive document image Binarization combines the local image contrast with the local image gradient to derive an adaptive local image contrast in order to overcome the normalization problem.
Figure 1: examples of degraded document images
3.1.1. Contrast Image Construction Local maximum and minimum filter extends the previous Bunsen technique which calculates the image contrast based on the maximum and minimum intensities within the local neighbor window. In Bunsen paper the local contrast is evaluated as follows.
III. RELATED WORK
C (I,J)=I MAXIMUM (I,J) – I MINIMUM (I,J)
There are several document image Binarization contests (DIBCO) based on different methodologies developed by the various researchers. The various methods and systems are summarized as follows.
Where C (I , J)=Local image contrast. The local image contrast evaluated by local maximum and minimum is shown below
C (I, J) =
3.1. Binarization of degraded document images by local maximum and minimum filter.
( , )– ( ,)
(1)
( ,) ( ,) €
(2)
Where
€ is a positive small number that is added when local maximum is equal to 0.
3.2. Ni-black approach “An Introduction to digital image processing”.
3.1.2. High Contrast Pixel Detection 3.1. LOCAL MAXIMUM AND MINIMUM: As the local image contrast and gradient are determined by the difference between the maximum and minimum intensities within the local window then the pixels at both sides of text stroke will be selected as high contrast pixels. The binary map can be improved by the combination edges with the canny edge detector. Image gradient plays important role in edge detection. Image
Local Maximum and Minimum (LMM) method makes use of image contrast that is calculated based on determining the distance between the maximum and minimum intensities of the pixels within the local neighborhood window. Compared with image gradient, image contrast evaluated by the Local Maximum and
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gradient indicates the sharp discontinuities in the gray scale image and indicates the directional changes on the intensities of the image pixels. The below figures indicate the image contrast and image gradient.
windowing technique .In Ni-blacks method the threshold value is calculated based in the mid-range values of the neighboring window. The Ni-blacks scheme consists of step wise procedure. Ni-blacks approach calculates the rough estimation of the foreground regions. Foreground pixels are a subset of Ni-blacks result and also introduces some noise. Ni-blacks algorithm calculates a threshold along the pixel by pixel by shifting a rectangular window across the image. The threshold T for the centre pixel of the window is calculated by using the mean M and variance V corresponding to the grey values in the window. T=M + k V Where Constant K=-0.2.
Figure 2 (a) Global thresholding of image gradient
(4)
The value of k is used to determine how much of object boundary is taken as a part of the given object. The brief methodology of the Ni-blacks approach is summarized below.
3.2.2. Pre-processing: Pre-processing is essential in order to remove the noise present in the gray scale source image, to smooth the background texture. Weiner filter is used to remove the noise and to increase the contrast between the text and background. The main purpose of the Weiner filter used in the filtering technique is for image restoration. The source image I sis transformed into grayscale image according to the below formula.
(b) Global thresholding of image contrast.
3.1.3. Threshold estimation: Thresholding determination from detected high contrast image pixels is based on two factors. First, the text pixels should be close to the high contrast image pixels. Second, the intensity of the text pixels should be smaller than the average intensity of the detected high contrast image pixels within the local window. The document text from the detected text stroke edge pixels can be extracted as follows. R(x, y) =
{1
I(x, y)=µ + (ð2-v2)(I s(x, y)-µ)/ ð2
Where µ is a local mean and ð2 is variance at N×M neighborhood window.3×3 Weiner filter is used for documents one to two pixel wide characters. Below figure shows the result of Weiner filter to a document image.
Ne≥ N minimum&&
I(x, y) ≤ E mean+ 0
(3)
Other-wise.
}
Figure 3: (a) Original image.
Where E mean and E standard are mean and standard deviation and are defined as follows
E mean=
∑ ( , )∗(
(5)
( , ))
N
E standard = (( ( , ) −
) ∗ 1 − ( , ) )2/2.
(b) 3×3 Weiner filter.
3.2. Ni-blacks Approach:
3.2.3. Estimation of the foreground regions:
3.2.1. Description:
Ni-blacks method is applied for adaptive thresholding, since it introduces some noise. At this stage image I(x, y) is processed to extract the binary image N(x, y) which
Ni-blacks approach to overcome the ambiguities present in the degraded document image through
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contains ones for estimated foreground region. Ni-blacks method makes use of Laplacian of Gaussian (LOG) filter to reduce the errors and having the disadvantage of responding to some of the existing edges.
Figure 4: (a) Original image.
(b): Histogram. The average distance DAverage between foreground and background can be calculated as follows DAverage=
(b) Estimation o foreground region.
3.2.4. Background Surface estimation
{I(x, y) ∑
( ( , )(1 − ( , ) ))
∑
∑
(6)
3.2.5. Threshold estimation: Threshold value is calculated by combining the background surface B with the original image I. Text is present when the distance of the original image calculated from background exceeds a threshold d. Threshold value will change according to the gray value of background surface.
0,
(9)
Imax(i, j) − Imin(i, j) Refers to local image gradient that is normalized to [0, 1].The local window size is empirically set to 3. Where is the weight between the local contrast and local gradient that is maintained based upon statistical information of the prescribed document. The power function has a nice property that it smoothly increases from 0 to1 and the shape of the curve can be easily controlled by different values of .The initial process in the Binarization system includes the high intensity pixel detection by constructing the adaptive contrast map.The next step is the text stroke edge pixel detection. Edge characterizes boundaries and edge detection reduces the amount of data and filters out useless information. The software developed by MATLAB 7.0 is nothing but canny edge detection algorithm performs better for all the scenarios. Canny edge detector is combined with the Otsu’s algorithm to extract the text stroke edge pixels properly. Canny edge detector will have the good localization property that it marks the edges close to real edges. Canny edge detector first filters the image to eliminate noise. Then it finds image gradient. Hysteresis is used to move along the pixels that have not been suppressed. Hysteresis uses two threshold values and if the magnitude is below the first threshold, it is set to 0. If the magnitude is above the high threshold it is mark as an edge. If magnitude lies between two thresholds, then it is set to 0 unless there is a path to another pixel lying above T. The Binarization performance are calculated by using F-measure, Peak Signal to Noise Ratio (PSNR), Distance Reciprocal Distortion.
If N(x, y)=1.
T(x, y) = {1,
(8)
C a (i,j) =δC(i, j) + (1 − δ)(Imax(i, j) − Imin(i, j))
(1 − ( ( , ))
}
( , ))
( , )
Image Binarization System has a lot of advantages over the previous existing systems. The Binarization system reduces the normalization factor that appears in the local maximum and minimum method by combining the local image contrast with the local image gradient which gives rise to adaptive local image contrast as shown below
If N(x, y) = 0.
∑
∑ ∑
IV.IMAGE BINARIZATION SYSTEM:
The pixels of source image I(x, y) belongs to background surface B(x, y) only if the pixels of the rough estimated foreground region image N(x, y) has zero values. B(x, y) =
∑ ∑ ( ( , )
if B(x, y)-I(x, y)>d (B(x, y)) other-wise.
} (7) Histogram for a document image is shown below with two peaks, one for text and another one for background region.
Figure 5: (a) Original image.
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V.SIMULATION RESULTS
[6] G. Leedham, C. Yan, K. Takru, J. Hadi, N. Tan, and L. Mian, “Comparison of some thresholding algorithms for text/background segmentation in difficult document images,” in Proc. Int. Conf. Document Anal. Recognit., vol. 13. 2003, pp. 859–864. [7]M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance Analysis,” J. Electron. Imag., vol. 13, no. 1, pp. 146–165, Jan. 2004. [8] O. D. Trier and A. K. Jain, “Goal-directed evaluation of binarization methods,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 12, pp. 1191–1201, Dec. 1995. [9] O. D. Trier and T. Taxt, “Evaluation of binarization methods for lowdocument images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 3, pp. 312–315, Mar. 1995. [10] A. Brink, “Thresholding of digital images using twodimensional entropies,” Pattern Recognit., vol. 25, no. 8, pp. 803–808, 1992.
FIGURE 6: F- measure performance graph VI. CONCLUSION The Binarization system involves several parameters that can be easily estimated based on the statics of the input document. When compared to the previous LMM method the Binarization system calculates threshold values each pixel by pixel instead of considering threshold value as value of the highest intensity pixel. When compared to NIBLACKS approach which calculates threshold values based on midrange value of the window size, where as in Binarization System threshold value depends on adjacent pixel distance which yields good results. The previous NIBLACKS approach makes use of LOG operator which has a disadvantage of malfunctioning at corners, curves. Image Binarization system makes use of canny operator which has an advantage of probability if finding error rate, improving SNR. The proposed enhanced system is stable and easy to work with degraded document images. The enhanced Binarization system avoids the normalization problem and involves in 0the contrast image enhancement and yields good results in several document analysis. VI .REFERENCES 1] B. Gatos, K. Ntirogiannis, and I. Pratikakis, “ICDAR 2009 document imagebinarization contest (DIBCO 2009),” in Proc. Int. Conf. Document Anal.Recognit., Jul. 2009, pp. 1375–1382. [2] I. Pratikakis, B. Gatos, and K. Ntirogiannis, “ICDAR 2011 document image binarization contest (DIBCO 2011),” in Proc. Int. Conf. Document Anal.Recognit., Sep. 2011, pp. 1506–1510. [3] I. Pratikakis, B. Gatos, and K. Ntirogiannis, “H-DIBCO 2010 document image binarization competition,” in Proc. Int. Conf. FrontiHandwrit.Recognit., Nov. 2010, pp. 727– 732. [4] S. Lu, B. Su, and C. L. Tan, “Document image binarization using background. [5] B. Su, S. Lu, and C. L. Tan, “Binarization of historical handwritten document images using filter method,” in Proc. Int. Workshop Document Anal. Syst., Jun. 2010, pp. 159–166.
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