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THE INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT2014)

University of Nizwa, Oman December 9-11, 2014 Page 84

Noise Removal Techniques For Arabic Handwriting

Roqyiah M.Abdeen Ahmed Z.Afifi Ashraf B.Elsisi

Computer Science dept., Faculty of Computers and Information, Menofia University, Egypt

[email protected] [email protected] [email protected]

Abstract: Noise reduction is a very important task for character recognition system. Image quality and execution time are

also important factors to choose the suitable noise reduction approach. Therefore, in this paper a comparative study of six

different noise reduction approaches for Arabic handwriting is presented. These Approaches are mean filter, median filter,

majority morphological filter, majority logic filter, coordinate morphological filter and coordinate logic filter. These filters

are tested on 30 gray scale image of Arabic words with two different noise types (salt & pepper and Gaussian noise) and three

levels of noise density. Mean Square Errors (MSE) and Peak-Signal to Noise Ratio (PSNR) are used to evaluate the image

quality of different approaches. The experimental results show that the morphological filter preserves the details of the image

and gives high PSNR value and low error rate; however, it requires more execution time. The logical filter on the other hand

requires less execution time; however, it gives low PSNR value. The coordinate morphological filter is half way between other

filters, it does not require a high execution time like morphological filter and the PSNR is not low as the logical filter. The

previous filters are tested also on three standard natural gray scale images and the results appear similar with the previous

results for images that contain Arabic handwritings.

Keywords: Noise reduction, Morphological filters, Logic filters,Mean Square Errors, Peak-Signal to Noise Ratio..

1. Introduction

Noise removal is an important topic used extensively in

the field of handwritten or machine-printed documents

analysis. For handwritten documents, the removal of

noise requires careful processing because some types of

noise look similar to certain characters or parts of

characters. Therefore, the noise removal technique may

remove an important feature from the character which

leads to a wrong classification result. Digital capture of

images can introduce noise from scanning devices and

transmission media. Prior to the character recognition,

it is necessary to eliminate these imperfections [9, 4].

When working with handwritten images, the noise can

divide to; low-level and high-level. Low-level noise is

produced by the hardware equipment during the

scanning process; it is a random variation of intensity in

document images. High-level noise refers to

undesirable parts of the image data for the intended

application, and as such they can be inherent parts of

the input data or artefacts that are produced by the

involved hardware equipment or the processing system

[4]. There are many common filtering methods for

noise removal such as the mean and median filters;

however, each one of these filters used to remove

certain types of noise. The median filter can be used to

suppress impulsive noise component while preserving

sharp edges; however, it often fails to provide sufficient

smoothing of non-impulsive noise component. The

mean filter smooth out the edges; but the impulsive

noise components cannot be suppressed sufficiently

[12]. In [13], an improved median filtering algorithm is

proposed. This algorithm uses the correlation of the

image to process the features of the filtering mask over

the image. It can resize the mask adaptively according

to noise levels of the mask. Generally, median filters

or nonlinear filters have been used for noise

reduction but these methods will destroy the natural

texture and important details in the image like the

edges. In [10], an efficient method for impulse

noise reduction from images using fuzzy cellular

automata has been proposed in two stages. In the

first stage, noisy pixels are detected by cellular

automata based on the statistical information. In the

second stage, this information is utilized to change

the noisy pixel by using the fuzzy cellular automata.

In addition to the importance of the image quality, the

fast execution time is required. The work in [11]

present a noise reduction method using Coordinate

Logic (CL) filters applied to printed text and

handwriting images. CL is a family of non-linear

filters, which are very fast in their hardware

implementation [11]. In this paper, a comparative

study between six filters for Arabic handwriting is

provided. These filters are mean filter, median filter,

majority morphological filter, majority logic filter,

coordinate morphological filter and coordinate logic

filter [1-3].

The rest of the paper is organized as following. In

Section 2, the image noise is defined. In Section 3, the

common filtering approaches used for noise reduction

is presented. In Section 4, the Coordinate logic filters

is presented. The experimental results and analysis are

discussed in Section 5. Finally, the conclusion and

future work is put forward in Section 6.

2. Image Noise Noise is an error occurs during the image acquisition

process that results in pixel values that do not reflect

the true intensities of the real scene [6]. Noise

removal algorithm is the process of removing or

reducing the noise from the image. Different

factors may be responsible for introduction of noise in

the image. Noise usually quantified by the percentage

of pixels which are corrupted. The main sources of



noise in the digital image are; The environmental

conditions may be affect the imaging sensor during

image acquisition, insufficient light levels and sensor

temperature may introduce the noise in the image,

electronic transmission of image data can make noise,

and if dust particles are present on the scanner screen,

they can also introduce noise in the image [5]. In

document images, there are two types of degradation:

The first one is physical degradation of the hardcopy

documents creation or storage and the second one is

degradation introduced by digitization. If any type of

them is severe enough, the performance of a document

analysis system can be reduced significantly [11].

There are two main differences between handwriting

and machine printed texts; the first difference is that the

handwriting in a document often indicates corrections,

additions, or other supplemental information that

should be treated differently from the main content and

the second difference is that the segmentation and

recognition techniques are significantly different for

machine printed and handwriting texts [15]. Image

noise can be classified to Impulse noise (Salt-and-

pepper noise), Amplifier noise (Gaussian noise),

Poisson Noise (Shot noise) , Uniform Noise

(quantization noise), and Speckle Noise (Multiplicative

Noise) [5].

Impulse Noise (Salt and Pepper Noise) contains

random occurrences of both black and white intensity

values [7]. An image containing salt-and-pepper noise

will have dark pixels in bright regions and bright pixels

in dark regions [8]. This type of noise can be caused by

dead pixels, analog-to-digital converter errors, and bit

errors in transmission.

Amplifier Noise (Gaussian noise) is also called the

normal noise is randomly occurs as white intensity

values [7]. Amplifier noise is a major part of the "read

noise" of an image sensor, that is, of the constant noise

level in dark areas of the image [8], Gaussian

distribution noise can be expressed by:

( )

( √ )

( )

( )

where, x is the grey level image, P(x) is the Gaussian

distribution noise in image x, µ the mean value and σ is

the standard deviation .

Poisson Noise (shot Noise) or Photon noise is a type

of electronic noise that occurs when the finite

number of particles that carry energy, such as

electrons in an electronic circuit or photons in an

optical device, is small enough to give rise to

detectable statistical fluctuations in a measurement.

Speckle Noise (Multiplicative Noise) can be modelled

by random value multiplications with pixel values of

the image [5].

3. Common Filtering Approaches For Noise

Reduction

Image filtering (de-noising) is very important task

in image processing for the analysis of images.

While numerous image filtering algorithms are

available, the best one must delete the largest

amount of noise while maintaining the contents and

details of the image. Filtering methods can be

classified to linear as well as non-linear. The linear

methods are fast enough, but they do not preserve the

details of the images, whereas the non-linear methods

preserve the details of the images [5].

3.1 Linear Filters Linear filter is used to remove certain types of noise.

A mean filter is an example of linear filter. The Mean

Filter (MF) is a simple linear filter, intuitive and easy

to implement method of smoothing images, i.e.

reducing the amount of intensity variation between

one pixel and the next. The idea of the mean filter is to

replace each pixel value in an image with the mean

value of its neighbours, including itself. This works to

delete some of the pixel values that represent

important information in the image, such as edges,

which are not identical with the values of

neighbouring pixels. Mean filtering depends on what

is usually called a convolution filter. Like other

convolutions it is based around a kernel, which

represents the shape and size of the neighbourhood to

be sampled when calculating the mean [7]. Often a

3×3 square kernel is used, although larger kernels (e.g.

5×5 squares) can be used for more severe smoothing.

(Note that a small kernel can be applied more than

once in order to produce a similar but not identical

effect as a single pass with a large kernel.)

There are two main problems with mean filtering:

If there is a pixel value with a very

unrepresentative value, it can significantly affect

the mean value of all the pixels in its

neighbourhood.

When the filter kernel reaches to an edge, the filter

will produce new values for pixels on the edge

and then will blur that edge. This may be a

problem if sharp edges are required in the output

image.

3.2 Non-Linear Filters Nonlinear filters behaviour is slightly different from

the linear filters. For nonlinear filters, the filter can

preserve edges, it is very effective at removing

impulsive noise, but it can be difficult to design.

Median filter (MDF) is a non-linear filter based on the

ranking of pixel values contained in the filter region.

Median filter is good for reducing certain types of

noise such as the salt and pepper noise. A major

advantage of this filter is that it can delete the values

of noise while preserving the important parts in the

image, such as edges [5]. Like the mean filter, the

median filter finding new considered pixel based on

the values of its neighbours but instead of simply

replacing the pixel value with the mean of

neighbouring pixel values, it replaces it with the



median of those values. The median is calculated by

first sorting all the pixel values from the surrounding

neighbourhood into numerical order and then replacing

the pixel being considered with the middle pixel value

and if the neighbourhood contains an even number of

pixels, the average of the two middle pixel values is

used.

The median filter has two main advantages over the

mean filter; The median is a more robust average than

the mean and so the pixel of the anomalous value from

the surrounding neighbours, whether it is too large or

too small, cannot affect the median value. Since the

median does not create new unrealistic pixel values, it

must actually be the value of one of the pixels in the

neighbourhood, for this reason the median filter is

much better at preserving sharp edges than the mean

filter.

One of the major problems with the median filter is that

it is relatively expensive and complex to compute. To

find the median it is necessary to sort all the values in

the neighbourhood into numerical order and this is

relatively slow.

4. Coordinate Logic Filters

Coordinate logic filters (CL) is a family of non-linear

filters, which are very fast in their hardware

implementation [11]. This filter depends on the direct

execution of logical operations (AND, OR) between the

binary values of the pixels in the image, which is

similar to the execution of (MIN, MAX) in the

morphological filters. That makes the CL filters to have

analogous functionality with the corresponding

morphological ones, but not the same [11].

CL Filters and their associated Coordinate Logic

Operations (CLOs) are used in many areas related to

image processing like noise removing, edge detection,

opening, closing, skeletonization, feature extraction,

and fractal modelling. CL filters are related to

morphological filters but constitute a separate class of

nonlinear filters. The difference between the

morphological filters and CL filters is that the

morphological filters may be considered as a class of

rank order filters, which involve some kind of sorting,

while the CL filters do not. The CL filters can execute

easily and fast the four basic morphological operations

(erosion, dilation, opening and closing). Therefore, the

filters are expected to be suitable for all the variety of

tasks that are executed by morphological filters [11, 3,

14].

Using coordinate logic operations in combination with

Boolean algebra, powerful filters for noise cleaning can

built [1]. In what follows the majority coordinate logic

(MCL) filter is presented. In the regular majority

function, the output of the function will be the pixel

value that has the majority in this neighbourhood. The

majority CL filter results by checking all the possible

combinations of k objects, taken (k + 1)/2 each time.

Thus the majority CL (MCL) filter implements quickly

and easily an approximation of the measure of the

majority value that is suitable for additive noise

removal [1,3]. In case an active neighbourhood of 5

pixels. One characteristic rhombus structuring element

is described by:

where [*] denotes the location of the origin (i, j)

and * denote the pixels in the structuring element.

The filter structure of a 2D CL filter, corresponding to

this rhombus structuring element, is:

f(i, j) =g(i-1,j)o g(i, j-1) o g(i, j)o g(i+1, j)o g(i, j+1) (2)

where(i ,j) is the location of the original pixel for the

raw i and column j, f(i, j) is the new output pixel in the

filtered image f after applying the majority CL filter

on the original gray scale image g . The specific

majority CL filter results by checking all the

possible combinations of 6 objects, taken 3 each

time, as follows:

( ) [ ( ) ( ) ( )] [ ( ) ( ) ( )] [ ( ) ( ) ( )] [ ( ) ( ) ( )]

[ ( ) ( ) ( )] [ ( ) ( ) ( )] [ ( ) ( ) ( )] [ ( ) ( ) ( )] [ ( ) ( ) ( )] ( )

where CAND and COR is the logical AND, OR

executed among the corresponding binary bits of

equal length of the considered pixels, without

counting the carry bits [3].

The Majority Morphological filter (MMF) is also

implemented using the morphological MIN and MAX

operations. Depend on the Theorem in [14]. Let there

be A, B with A < B. Then

E = A CAND B = min(A, B) = A

D = A COR B = max (A, B) = B (4)

Thus, the majority function can implemented by

replacing the CAND by the MIN operation and the

COR by the MAX operation. The difference between

these two functions is that to enables the CL filters to

behave exactly as the morphological ones it needs to

quantizing the image pixel intensity in usually fewer

gray scale levels than the original image.

Consequently, the new image intensities taking only

the specific decimal values defined bellow. The exact

number of the new allowed levels depends on the

image application at hand and on the available word

length for data representation.

The set of specially quantized decimal numbers is

given by:

{ } ( )



In this way the cardinality | | of set equals n+1. If

one uses n-bit words to store decimal numbers, then

she/he can store only n+1 such specially quantized

numbers.

Let , , , .... , i.e. they are numbers

equal to one of the values 0, 1, 3, 7,.... , , where n

is the length of the binary word. Then for the binary

representations of it holds that

E = CAND ... CAND = min

{ (6)

D= COR ... COR = max

{ ( )

where CAND and COR are the coordinate logic AND,

and OR operations between the binary representation of

the pixel decimal values of the image. Since CAND and

COR operators, when operating on numbers belonging

to are identical to the MIN and MAX operators,

respectively [14].

Another Coordinate logic (CL) filters is presented

in[11] perform a combination of erosion and dilation

operation using logical operations, first if we consider

an image I with M×N pixels which M and N are the

numbers of image rows and columns, respectively,

matrix elements of image I well defined as below:

( ) ∑ ( ( ))

( )

Where S(i,j) is the decimal values of matrix elements of

image I. Sp(i,j), p=0,1,...,n-1 are the binary components

of the decimal pixel values. S(i,j), i = 1,2,...,M; j =

1,2,…,N and n is the bits number of binary S(i,j). The

first step for noise reduction, according to equation ( )

the image I has been defined as a matrix, which consist

of binary elements of the original image (decimal

matrix elements of image I must be converted to binary

elements). Since binary conversion affects all matrix

elements, image noise pixels have been also converted

under this conversion. Now, the noise will be separated

using erosion operation as follows:

( ) ∑ ( ( ))

( )

Where Erosion (Sp(i,j)) is the erosion of matrix

elements of image I. The erosion operation on an image

is equal to the coordinate logic AND operator. Equation

(10) can be considered as the coordinate logic AND

operation on all matrix elements of original image

based on the rhombus structuring element and the filter

structure of equation (3). Using this filter structure,

the erosion of the image I using CL filters is given

by :

(i, j) = I(i - 1, j) CAND I (i, j - 1) CAND I

(i, j) CAND I (i + 1, j) CAND I (i, j +1). (10)

The CL-based erosion tends to remove small objects

and small projections, whereas dilation tends to fill

holes and concavities in objects. A dilation

followed by an erosion tends to fill holes and

concavities without change the overall size of an

object [1].

Thus, the next step after noise separation using the

erosion operation is the dilation, the image will be

dilated to fill the holes which is leaved by the erosion

step. is Dilation matrix of Sp(i,j) which is expressed

as:

( ) ∑ ( ( ))

( )

The dilation operation on an image is equal to the

coordinate logic OR operator , Using the rhombus

structuring element, the dilation of the image I

using CL filters is given by :

(i, j) = I (i - 1, j) COR I(i, j - 1) COR

I(i, j) COR I(i+1,j) COR I(i,j+1). (12)

Finally, the proposed filter for noise removal is

presented as:

( ) [ ( ) ( ) ( )]

[ ( ) ( ) ( )] ( )

Where and are coordinate logic OR and AND

operators, respectively.

The CLOs of erosion and dilation act on the specific

set described in equation (6) exactly as the MIN and

MAX operators of morphological filtering [14]. The

same steps of the CLF are applied, but the erosion of

image equation will be:

(i, j) = MAX [ I(i - 1, j) , I (i, j - 1) , I (i, j)

, I (i + 1, j) , I (i, j + 1)]. (14)

And the dilation equation is formed as:

(i, j) = MIN [ I (i - 1, j) , I(i, j - 1) , I(i, j) ,

I(i + 1, j) , I(i, j + 1)]. (15)

The final proposed filter equation will be as follows:

( ) ( [ ( ( ) ( ) (

))] [ ( ( ) ( ) (

))] ( )



5. Experimental Results and Analysis

Mean Filter (MF), Median Filter (MDF), Majority

Logic Filter (MLF), Majority Morphological Filter

(MMF), Coordinate Logic Filter (CLF) based on

erosion and dilation and Coordinate Morphological

Filter (CMF) based on the morphological erosion and

dilation were implemented using the Math Works

™MATLAB® 2012 software and tested on an Intel

(R) Core(TM) i5 processor with a clock speed of 2.50

GHz and 6 GB main memory with Windows 7

Ultimate operating system. Salt & Pepper noise and

Gaussian noise are used for testing. These noise types

are applied with three different densities (0.02, 0.06

and 0.1) for Salt & Pepper and with mean =0 for the

Gaussian noise. These noises are applied on 30

images contains Arabic handwriting words, then the 6

filters are implemented and used to remove the noise

from these images. The performance of these six

filters is evaluated by three criteria: the PSNR (Peak

Signal to Noise Ratio), MSE (Mean Square Error) and

the execution time . The MSE is calculated using this

formula:

∑∑( ( ) ( ))

( )

Where ( ) represents the original (reference)

image and ( ) represents the distorted (modified)

image and i and j are the pixel position of the M×N

image. The PSNR is evaluated in decibels and is

inversely proportional the Mean Squared Error [2]. It

is given by the equation:

( )

√ ( )

Figure. 1 shows the average execution time in seconds

by the six filters on 30 images corrupted by three

levels of salt & pepper noise. As can be seen from

Figure .1, the filters that depend on the logical

operations (the Majority Logic Filter and Coordinate

Logic Filter) are executed faster than the other filters.

The mean filter is a linear filter it also consumes less

time but it comes after the logical filters, then the

coordinate morphological filter then the median filter

and finally the majority morphological filter. Thus,

the median filter and the Majority Morphological

filter require more execution time than other filters to

remove salt & pepper noise. The same result appears

when applying the Gaussian noise as shown in Figure

2. In Figure 2 also the logical filters consume less

time than other filters then the mean filter and then the

Coordinate Morphological filter, also the Median

filter and the Morphological filter still take more time

than all the other filters.

Fig.1 Average execution time in case of salt & pepper noise.

Accordingly , We conclude that the logical filters is

very fast compared to the other filters and the mean

filter is come after them, the filter that use the

combination of morphological erosion and dilation is

the third one but the median and majority

morphological filters which do more computation

consume more time than the others. But, on the other

hand, when we compare the quality of the image the

result will changed. Figure3 shows the PSNR value

for the sex filters when applying salt & pepper noise

with 3 different levels.

Fig.2 Average execution time in case of Gaussian noise.

As can be noticed in Figure 3, the Majority

Morphological filter which take large execution time

gives the largest PSNR value, the median filter which

come after the MMF in the large execution time is

also gives large PSNR value then the CMF. The

fastest filters (logical filters and mean filter) gives a

small PSNR value compared to all the other filters.

Fig.3 PSNR in case of Salt & Pepper noise.

The Coordinate Morphological Filter (CMF) is in the

half way among others, it does not consume a lot of

time like the MDF and the MMF and the PSNR

value is not small as the logical and mean filters. It is

also do better with the Gaussian noise as shown in

Figure 4. Where the CMF is get the largest PSNR



value than the other filter and the MMF is fail with

this type of noise so we conclude that the MMF is

very good only for removing salt & pepper noise. In

Figure 5.a, the original image is corrupted by 0.06

density level of salt & pepper noise. The first two

filters which give a high PSNR value is MMF and

MDF as shown in Figure 5, where Figure 5.b is the

image filtered using the MMF and Fig 5.c is the image

filtered using MDF.

Fig.4 PSNR value in case of Gaussian noise

Now, the third filter which has high PSNR after the

MMF and MDF is the MCF. Figure 6.b shows the

image before and after applying the MCF. Using the

CMF the noise is removed completely but the text is

affected and small changes appeared in the text. The

mean and logical filters are consumed less time than

the other filters but the image is smoothed and some

noise still appears in the text. Figure 6.c shows the

image after using the mean filter. The logical filters

require some quantization process before applying the

filters. Figure 7 shows the quantized image and the

images after removing the noises using the MLF and

CLF. As a result, the Coordinate Filters

(Morphological and Logical) have a good result in the

consumption time and PSNR value with salt & pepper

noise. The second type of noise is the Gaussian noise,

the behavior of many filters will changed because the

Gaussian noise is different from the salt & pepper

noise, the salt & pepper noise just add new white and

black pixels while the Gaussian noise blur the image

and destroy the details of the image more than the salt

& pepper noise. As we can see from Figure 4, the

PSNR value for all filters is slightly closed to each

other; the CMF gives high PSNR value then the

median filter then the MMF and the mean and logical

filters in the last. Fig. 8 show the image corrupted

with Gaussian noise σ=0.06 and the image after

applying the first two filters (CMF and MDF), Fig 9

show the result of the MMF and the MF. Fig 10

shows the result of the two logical filters after

quantizing the image.

(a) (b) (c)

Fig.5 a) Image corrupted with 0.06 density of salt & pepper noise, b) Image

after applying MMF, c) Image after applying MDF.

(a) (b) (c) Fig.6 a) Image corrupted with 0.06 density of salt & pepper noise, b) Image

after applying CMF, c) Image after applying the MF.

(a) (b) (c)

Fig.7 a) Quantized image corrupted with 0.06 density of salt & pepper

noise, b) Image after applying CLF, c) Image after applying the CMF.

(a) (b) (c)

Fig.8 a) Image corrupted with 0.06 density of Gaussian noise, b) Image

after applying CMF, c) Image after applying MDF.

(a) (b) (c)

Fig.9 a) Image corrupted with 0.06 density of Gaussian noise, b) Image

after applying MMF, c) Image after applying MF.

(a) (b) (c) Fig.10 a) Quantized image corrupted with 0.06 density of Gaussian noise,

b) Image after applying MLF, c) Image after applying CLF.

Fig 11 and Fig 12 show the error rate for each filter

using the MSE metric, the error rate is increased when

the PSNR value is small, Thus the error rate is high

with the logical filters and the mean filters while it is

low with the MDF and the MMF and also the CMF is

in the middle .

Fig.11 Average MSE value in case of Salt & Pepper noise.

Fig.12 Average MSE value in case of Gaussian noise.



Now the previous filters will tested on three standard

natural images :the Baboon image, the cameraman

image and the pepper image after corrupting them

with 0.02 density of salt & pepper noise and Gaussian

noise then the time ,PSNR and MSE metrics are

calculated and compared with the results of the Arabic

handwriting images. Figure 13 show the original

standard images. The Figures (14-22) shows the

images after applying the six filters, and the Figures

(23-25) show the average time, PSNR and MSE

values. From the previous figures the results appear

very similar with handwritten images results.

(a) (b) (c)

Fig.13 The original images

(a) (b) (c)

Fig.14 a) Image corrupted with 0.02 density of salt & pepper noise, b)image

after applying MMF, c) image after applying the MDF

(a) (b) (c) Fig.15 a) Image corrupted with 0.02 density of salt & pepper noise, b) Image

after applying MMF, c) Image after applying the MDF

(a) (b) (c)

Fig.16 a) Image corrupted with 0.02 density of salt & pepper noise, b) Image

after applying MMF, c) Image after applying the MDF

(a) (b) (c)

Fig.17 a) Pepper image after applying CMF, b) Cameraman image after

applying CMF, c) Baboon image after applying the CMF

(a) (b) (c)

Fig.18 a) Pepper image after applying MF, b) Cameraman image after

applying MF, c) Baboon image after applying the MF

(a) (b) (c) (d)

Fig.19 a) Original quantized image, b) The quantized image corrupted with 0.02 salt & pepper noise , c) The image after applying the CLF d) The

image after applying MLF

Fig.20 Images corrupted with 0.02 density of Gaussian noise.

(a) (b) (c) (d)

Fig.21 a) The cameraman image after applying the MMF b) MDF, c)

CMF, d) MF..

(a) (b) (c) (d)

Fig.22 a) Original quantized image, b)the quantized image corrupted

with 0.02 salt & pepper noise , c) The image after applying the CLF d)

The image after applying MLF

Fig.23 The average PSNR value

Fig.24 The average MSE value



Fig.25 The average consumed time

6. Conclusion And Future Work

In this paper a comparative study of six filters (Mean

Filter (MF), Median Filter (MDF), Majority Logic

Filter (MLF), Majority Morphological Filter (MMF),

Coordinate Logic Filter (CLF) and Coordinate

Morphological Filter (CMF)) is presented. Their

performance is evaluated on 30 images containing

Arabic handwriting words with three density levels of

salt & pepper noise and with three different variances

of Gaussian noise. Execution time, the PSNR and MSE

values are calculated. Logical filters (MLF, CLF)

doesn't require a high execution time but the PSNR is

low compared to the morphological filters and the MSE

value is high. The MF is come in second after the

logical filter in the execution time, it requires short time

for execution but also the PSNR is low. The MMF

gives the highset PSNR value with the salt & pepper

noise but the PSNR result become low with the

Gaussian noise, the MMF requires a lot of time for

execution. The MDF gives high PSNR result with the

two types of noises but also it requires a high

execution time. Finally, the MCF gives a compromise

result, the PSNR value is not high like the

morphological filters but also not very low like the

logical filters and also the time is not high as the

morphological filters and not low as the logical filters,

this filter may represent a good choice for the character

recognition system if both time and the image quality is

important. All the previous six filters are also applied

on three standard natural images and the results are

look similar with the results of the Arabic handwriting

images. As future work, the previous filters will

included in an Arabic recognition system and the

recognition accuracy is compared after and before

using these filters to confirm how these filters affects

the recognition accuracy .

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