Document Image Segmentation Using Edge Detection
Method
1Manish
T. Wanjari
2Keshao
D. Kalaskar
Department of Computer
Science,
SSESA’s, Science College,
Congress Nagar, Nagpur,
Department of Computer
Science,
Dr. Ambedkar College,
Chandrapur, (MH), India
(MH), India
Keshao_kalaskar@yahoo.
co.in
mwanjari9@gmail.com
ABSTRACT
Document image segmentation is one of the most
important aspect in the analysis of document images. A
number of segmentation techniques are available which
fulfills the requirement for specific type of application of
Document Image Analysis. Edge detection is a fundamental
tool in image processing, machine vision and computer vision,
particularly in the areas of feature detection. Edge-detection
method is used for performing document image segmentation
tasks. In this paper various edge detection methods based on
Gradient and Laplacian such as Prewitt, Sobel, Roberts,
Canny, Zerocrossing, Laplacian of Gaussian edge detection
are implemented along with their advantages and
disadvantages. Lastly, experimental result & performance of
the edge detection method is compared.
Keywords
Image Processing (IP), Document Image Analysis (DIA),
Edge Detection, Segmentation, Sobel Operator, Canny
Operator.
1. INTRODUCTION
Edge detection method is a set of mathematical
methods which goal at identifying points in a digital
image at which the image brightness changes sharply
or, more formally has, discontinuities. The points at
which image brightness changes sharply are typically
organized into a set of curved line segments called
edges. [1]
Document Image segmentation algorithms
generally are based on one of two basic properties of
intensity values such as discontinuity and similarity. In
the first category, the approach is to partition a
document image based on abrupt changes in intensity,
such as edges in a document images. The principal
approaches in the second category are based on
partitioning a document image into regions that are
similar according to a set of predefine criteria.
In this paper we discuss a number of
approaches in the first category mentioned. We begin
the development with methods suitable for detecting
3Mahendra
P. Dhore
Department of Electronics &
Computer Science,
R.T.M. Nagpur University
Campus,
Nagpur, (MH), India
mpdhore@rediffmail.com
gray level discontinuities such as points, lines and
edges. Edge detection in particular has been a staple for
many years. In addition to edge detection, we also
discuss methods for connecting edge segments and for
‘assembling’ edges into region boundaries. [2]
Edge detection is one of the most important
element in document image analysis and image
processing in computer vision because they apply sound
significant role in many applications of image
processing particular for machine vision. Edge
detection is a method of determining the discontinuities
in gray level or binary document images. Edge
detection method is the common approach for detecting
meaningful discontinuities in the gray level. Document
Image
segmentation
methods
for
detecting
discontinuities are boundary based methods. Edges are
local changes in the document image intensity. Edges
typically occur on the boundary between two regions.
Important features can be extracted from the edges of a
document image (e.g., corners, lines, curves). Edge
detection is an important feature for document image
analysis. These features are used by higher-level
computer vision algorithms (e.g., recognition). Edge
detection is used for object detection which includes
various applications such as medical image processing,
biometrics etc. Edge detection is an active area of
research as it facilitates higher level document image
analysis. There are three different types of
discontinuities in the grey level like point, line and
edges. Spatial masks can be used to detect all the three
types of discontinuities in a document images. And also
we discuss various operators uses the first and second
order derivatives of edge detection methods. [3, 4]
In a document image an edge is a curve that a
path of rapid change in document image intensity.
Edges are often associated with the boundaries of object
is a scene. Edge detection method is used to identify the
edges in a document images. The first derivatives of the
intensity is larger in magnitude than some threshold and
the second order derivative of the intensity has a zero
crossing edge provides a number of derivative
estimator. [5, 6]
2. DETECTION OF DISCONTINUTIES
It deals with a various methods for detecting
the three basic types of gray-level discontinuities in a
digital image: points, line and edges.
a.
b.
c.
Point Detection: The detection of isolated
points (a point whose gray level is
significantly different from its background and
which is located in a homogeneous or nearly
homogeneous area) in a document image is
straightforward in principle.
Line Detection: The next level of complexity
is line detection. The document image
intensity abruptly changes value but then
returns to the starting value within some short
distance (generally used lines).
Edge Detection: Although point and line
detection are important in any part of
segmentation, edge detection is by far the
most common approach for detecting
meaningful discontinuities in gray level.[1]
3. EDGE DETECTION METHODS
The edge representation of a document image
reduces the quantity of data to be processed; it retains
necessary information regarding the shape of character
in document image. There are many edge detection
methods in the literature for document image
segmentation. Most of the used discontinuity based
edge detection methods are reviewed in this paper.
Those methods are Prewitt, Sobel, Canny, Roberts,
Zerocross and Laplacian of Gaussian. [7]
Although point and line detection certainly are
important in any part of segmentation, edge detection is
the most common approach for detecting meaningful
discontinuities in gray level. In this paper we discuss
approaches for implementing first and second order
digital derivatives for the detection of edges in a
document image.
Edge detection is an unsolved problem, so,
there is no proper solution. However, edge detection
provides rich information about the scene being
observed. Many researchers have been taking advantage
of edge detection information to improve the
segmentation of range document images by integrating
edge detection with other different segmentation
methods or operators are discussed as follows.
3.1. Prewitt edge detection
The Prewitt edge detector is an appropriate
way to estimate the magnitude and orientation of an
edge. Although differential gradient edge detection
needs a rather time consuming calculation to estimate
the orientation from the magnitudes in the x and ydirections, the compass edge detection obtains the
orientation directly from the kernel with the maximum
response. The prewitt operator is limited to 8 possible
orientations, however experience shows that most direct
orientation estimates are not much more accurate. This
gradient based edge detector is estimated in the 3x3
neighborhood for eight directions. [8]
In edge detection method, the prewitt masks
are simpler to implement than the Sobel masks, but the
later have slightly superior noise suppression
characteristics, an important issue when dealing with
derivatives.
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0
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0
0
0
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0
+1
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+1
+1
-1
0
+1
Gx
Gy
3.2. Canny edge detection
The Canny edge detector is regarded as one of
the best and standard edge detectors recently in use;
Canny’s edge detector ensures good noise immunity
and at the same time detects true edge points with
minimum error. The Canny edge detector is an edge
detection operator that uses a multi-stage algorithm to
detect a wide range of edges in document images. It is
developed by John Canny considered the mathematical
problem of deriving an optimal smoothing filter given
the criteria of detection, localization and minimizing
multiple responses to a single edge. He showed that the
optimal filter given these assumptions is a sum of four
exponential terms. He also showed that this filter can be
well approximated by first-order derivatives of
Gaussians. Canny also introduced the notion of nonmaximum suppression, which means that given the
presmoothing filters, edge points are defined as points
where the gradient magnitude assumes a local
maximum in the gradient direction. Looking for the
zero crossing of the 2nd derivative along the gradient
direction was first proposed by Haralick.[9] It took less
than two decades to find a modern geometric variational
meaning for that operator that links it to the MarrHildreth (zero crossing of the Laplacian) edge detector.
This observation was presented by Ron Kimmel and
Alfred Bruckstein. [10]
Although his work was done in the early days
of computer vision, the Canny edge detector (including
its variations) is still a state-of-the-art edge detector.[11]
Unless the preconditions are particularly suitable, it is
hard to find an edge detector that performs significantly
better than the Canny edge detector. The CannyDeriche detector was derived from mathematical
criteria as the Canny edge detector, although starting
from a discrete viewpoint and then leading to a set of
recursive filters for document image smoothing instead
of exponential filters or Gaussian filters. [12]
other Euclidean distance-based operators. The Sobel
edge detection operator will be applied to the different
color space.
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0
-1
3.3. Roberts edge detection
0
0
0
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0
+2
The Roberts cross operator performs a simple,
quick to compute, 2-D spatial gradient measurement on
an image. It highlights regions of high spatial frequency
which corresponds to edges. In its most common usage,
the input to the operator is a grayscale image, as is the
output. Pixel values at each point in the output represent
the estimated absolute magnitude of the spatial gradient
of the input image at that point.
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0
+1
Gradient can be defined as
∇𝑓(𝑥, 𝑦) = 𝐺(𝑥, 𝑦) = √𝐺𝑥 2 + 𝐺𝑦 2 ----- (I)
Where f(x, y) is point in the original document image,
Gx(x, y) is a point in an document image formed by
convolving with first kernel and Gy(x, y) is a point in an
document image formed by convolving with second
kernel.
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0
0
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0
+1
+1
0
Gx
Gy
3.4. Sobel edge detection
Sobel operator or method is commonly used in
edge detection method. Sobel operator has been
researched for parallelism but sobel operator locating
complex edges are not accurate. It has been researched
for the sobel enhancement operator in order to locate
the edge more accurate and less sensitivity to noise but
software cannot get the real time requirement. [4]
The Sobel operator is a well known edge
detector method [13]. The original difference-based
gradient computation is replaced by a Euclidean
distance calculation. This vectorization of the algorithm
allows for the effective use of the color information
given that simple intensity differences would not
represent differences between two color vectors as well
as a Euclidean distance calculation.
The Sobel operator has been shown to be a
good edge detector. In its expanded form, it will deal
better with the information contained in color images
without compromising it such as in methods where the
operator is applied to each color plane independently
[14]. In this case, the correlation between the various
planes is lost and the final result would be probably less
than adequate. The Sobel operator will suffer from an
inability to identify all difference-based edges just as
Gx
Gy
The kernel can be applied separately to the input
document image, to produce separate measurement of
the gradient component in each orientation, Gx and Gy.
The gradient magnitude is given by
|𝐺| = √𝐺𝑥 2 + 𝐺𝑦 ----- (II)
An approximate Magnitude is computed using
|G| = |Gx| + |Gy| ----- (III)
3.5. Zerocross edge detection
It uses Laplacian of the second derivative and
it includes Laplacian operator. It is having fixed
characteristics in all directions and sensitive to noise.
Haralick proposed the use of zero-crossing of the
second directional derivative of the image intensity
function.
The edges determined by zerocrossing form
numerous closed loops. Zerocrossing methods are of
interest because of their noise reduction capabilities and
potential for rugged performance.
3.6. Laplacian of Gaussian edge detection
The Laplacian of Gaussian was proposed by
Marr (1982). The LOG of a document image f(x, y) is a
second order derivative defined as
∇2 𝑓 =
𝜕2 𝑓
𝜕𝑥 2
+
𝜕2 𝑓
𝜕𝑦 2
----- (IV)
The purpose of the Laplacian operator is to
provide a document image with zero crossing used to
establish the location of edges. In 1980, was invented
by Marr and Hildreth. The Gaussian filtering is
combined with Laplacian to break down the document
image where the intensity varies to detect the edges
effectively. The Laplacian is a 2-D isotropic measure of
the 2nd spatial derivative of a document image. The
Laplacian of a document image highlights regions of
rapid intensity change and is therefore often used for
edge detection. The Laplacian is often applied to a
document image that has first been smoothed with
something approximating a Gaussian Smoothing filter
in order to reduce its sensitivity to noise. The operator
normally takes a single gray level image as input.
It has two ways, it smoothes the document
image and it computes the Lapalcian, which yield a
double edge image. The digital implementation of the
Laplacian function is made through the mask as above;
the Laplacian is basically used to found whether a pixel
is on the dark or light side of an edge.
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Gx
Localization
and response.
Better detection
specially in
noise conditions
consuming
5. EXPERIMENTAL RESULT
The edge detection methods were implemented
by using MATLAB R2011b and tested with a document
image from UCDAR-UK Database. The objective is to
produce a clean edge map by extracting the principal
edge features of the document image.
Gy
4. ADVANTAGES AND
DISADVANTAGES OF EDGE
DETECTION TECHNIQUE
Edge detection is the fundamental part in
computer vision; it is need to point out the true edges to
get the output from the observation. We present some of
advantages and disadvantages on the basis of
implemented edge detection methods are as follows.
[15, 16]
Operator
Advantages
Disadvantages
Classical
(Sobel,
Prewitt)
Simplicity,
Detection of
edges & their
orientation
Sensitivity to
noise, Inaccurate
Zero
Crossing
(Laplacian,
Second
directional
derivative)
Detection of
edges and their
orientations.
Having fixed
characteristic in
all directions
Responding to
some of the
existing edges,
sensitivity to
noise
Laplacian
of
Gaussian(L
oG)
Finding the
correct places
of edges,
Testing wider
area around the
pixel
Malfunctioning
all the corners
curves and where
the gray level
intensity function
varies. No finding
the orientation of
edge because of
using the
Laplacian filter
Gaussian
(Canny)
Using
probability for
finding error
rate.
Complex
Computations,
False zero
crossing, Time
There are many ways to perform edge
detection for the document images. The edge detection
method may be grouped into two categories Gradient
and Laplacian. The gradient method detects the edges
by looking for the maximum and minimum in the first
derivatives of the document image. The Laplacian
method searches for the zerocrossing in the second
derivative of the document image to find edges. The
original document image obtained by using different
edge detection methods are given in figure.
(a)
(c)
(e)
(b)
(d)
(f)
methods. Robert edge detectors highlights the regions
of high spatial frequency which corresponds to edges.
Zerocrossing edge detector is having the fixed
characteristics in all directions and sensitive to noise.
The Laplacian is applied to a document image that has
first been smoothed with something approximating a
Gaussian Smoothing filter in order to reduce its
sensitivity to noise.
(g)
(h)
Fig. (a) Original document Image (b) Grayscale Image (c)
Prewitt method (d) Sobel method (e) Canny method (f)
Roberts method (g) Zerocross method (h) Laplacian of
Gaussian mehtod.
The classical methods for accurate detection of
edge features, as exemplified by canny operators,
demands such expensive operation as iterative use of
Gaussian, Laplacian.
In the above figure, We considered Color
Document Image as input from UCDAR-UK database
and it is converted into grayscale or a binary document
image, and returns a binary document image of the
same size, with 1’s where the function finds edges in
image and 0’s elsewhere. Figure shows (a) Original
Document Image (b) Grayscale or a binary document
image (c) The Prewitt method finds edges using the
prewitt approximation to the derivative. It returns edges
at those points where the gradient of document image is
maximum. (d) The Sobel method finds edges using the
sobel approximation to the derivative. It returns edges
at those points where the gradient of document image is
maximum. (e) the most powerful edge detection method
that edge provides is the canny method. The Canny
method finds edges by looking for local maxima of the
gradient of a document image. The gradient is
calculated using the derivative of a Gaussian filter. The
method uses two thresholds, to detect strong and weak
edges, and includes the weak edges in the output if they
are connected to strong edges. This method is therefore
less likely than the others to be fooled by noise, and to
detect true weak edges. This method’s result is better
than all other methods of edge detection technique. (f)
The Roberts method finds edges using the Roberts
approximation to the derivative. It returns edges at
those points where the gradient of document image is
maximum. (g) The Zerocross method finds edges by
looking for zero crossing after filtering document image
with a filter specify. (h) The Laplacian of Gaussian
method finds edges by looking for zero crossing after
filtering document image with a Laplacian of Gaussian
filter.
7. CONCLUSION
6. COMPARISON OF EDGE
DETECTION
Edge detection is a challenging problem. The
Prewitt and Sobel edge detector is a simple and
appropriate way to estimate the magnitude and
orientation of an edge and it is well known edge
detectors. Canny edge detector is one of the best,
standard and powerful edge detectors; its results are
best as compared to other edge detectors or the
In this paper we have implemented document
image segmentation based on edge detection methods
both of the gradient and laplacian. Edge Detection
provides more information about the document image
being detected. The sobel edge detector or operator has
been good edge detector as shown in above result. The
canny edge detector is one of the best edge detection
methods for the better performance, standard and more
powerful as compared to other edge detection methods.
8. ACKNOWLEDGEMENTS
The authors are thankful to the University
Grant Commission, New Delhi for supporting this work
as a part of Major Research Project.
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