Download Offline Arabic Character Recognition using Genetic Approach

Offline Arabic Character Recognition using
Genetic Approach: A Survey
Hanan Abdulrahman Aljuaid
Dzulkifli Muhamad
UTM
UTM
E-mail: [email protected]
E-mail: [email protected]
of recognizing individual characters in the word or holistic
approach of dealing with the entire word image as a whole.
Analytical approaches (e.g. Kim and Govindaraju, 1997; ElYacoubi et al., 1999; Koerich et al., 2003; El-Hajj et al., 2005;
Benouareth et al., 2006) basically have two steps, segmentation
and combination.
ABSTRACT
This study reports a survey on off-line Arabic character
recognition. It cost a minor light on characteristics of Arabic
writing. This paper also presents a general concept on the
recognition processes involved in the entire system of the
Arabic Character Recognition. It also includes some studies on
the works done in related issues based on Genetic Algorithms.
First the input image is segmented into units no bigger than
characters. Then segments are combined to match character
models using dynamic programming. Holistic approaches
(Madhvanath and Govindaraju, 2001) deal with the entire input
image. Holistic features like translation/rotation invariant
quantities, word length, connected components, ascenders,
descenders; dots, etc. are usually used to eliminate less likely
choices in the lexicon. Since holistic models must be trained for
every word in the lexicon, compared against analytical models
that need only be trained for every character, their application is
limited to those with small and constant lexicons, such as
reading the courtesy amount on bank checks (Farah et al., 2006;
Souici and Sellami, 2006).
Keywords
Arabic character Recognition, Offline, |Genetic Algorithms
1.
. INTRODUCTION/BACKGROUND
Character Recognition (CR) mechanization occupies an
intensive research region of the pattern recognition research
area. CR means translating images of characters into a text, in
other words, it represents an attempt to simulate the human
reading process. In other said, Handwriting recognition is a very
challenging task due to the existence of many difficulties such
as the high variability of the handwritten styles and shapes,
uncertainty of human writing, writing skew or slant,
segmentation of the words into characters and the size of the
lexicon.
While exploring many different methods, the use of genetic
algorithm to recognize a character has been a new algorithm
used in this problem. Genetic algorithms offer a particularly
attractive approach for this kind of problems since they are
generally quite effective for rapid global search. Moreover,
genetic algorithms are very effective in solving large-scale
problems, but what is the Gas?
The problem of handwriting recognition can be classified into
two main groups, off-line and on-line recognition, according to
the format of handwriting inputs. In offline recognition, only
the image of the handwriting is available, while in the on-line
case temporal information such as pen tip coordinates, as a
function of time, is also available. Many applications require
off-line HWR capabilities such as bank processing, mail
sorting, document archiving, commercial form-reading, office
automation, etc. So far, off-line HWR remains an open
problem, in spite of a dramatic boost of research (Koerich et al.,
2003; Plamondon and Srihari, 2000; Vinciarelli, 2002) in this
field and the latest improvement in recognition methodologies
(El-Yacoubi et al., 1999, 2002; Vinciarelli et al., 2004).
Genetic Algorithm (GAS) is a search technique used in
computer science to find approximate solutions to optimization
and search problems and is inspired by evolutionary biology
such as inheritance, mutation, natural selection, and
recombination. Genetic algorithms are typically implemented as
a computer simulation in which a population of abstract
representations of candidate solutions to an optimization
problem evolves toward better solutions. Traditionally,
solutions are represented in binary as strings of 0s and 1s, but
different encodings are also possible. The evolution starts from
a population of completely random individuals and happens in
generations. In each generation, the fitness of the whole
population is evaluated, multiple individuals are stochastically
selected from the current population (based on their fitness),
modified (mutated or recombined) to form a new population,
which becomes current in the next iteration of the algorithm as
show in figure 1.1. So, evolutionary algorithms work on
populations, instead of single solutions. In this way the search is
performed in a parallel manner.
Studies in Arabic handwriting recognition, although not as
advanced as those devoted to other scripts (e.g. Latin), have
recently shown renewed interest (Amin, 1998; Ben Amara and
Bouslama, 2003; Lorigo and Govindaraju, 2006). We point out
that the techniques developed for Latin HWR are not
appropriate for Arabic handwriting because, Arabic script is
based on an alphabet and rules distinct from those of Latin.
Since the word is the most natural unit of handwriting, its
recognition process can be done either by an analytic approach
71
Table 2.2: Different shape of Arabic Alphabet
2.
. LITERATURE REVIEW
2.4
GENERAL CHARACTERISTICS
OF ARABIC WRITING
The Arabic alphabet is the most important language not only for
Arab, but also for Muslim, because it is language for the holly
book Alquran. Although, the script of it used for writing several
languages of Asia and Africa, such as Arabic, Persian, and
Urdu. After the Latin alphabet, it is the second-most widely
used alphabet around the world. So, it is significant script need
to be recognized.
Several hard works have been devoted to recognition of cursive
script like Arabic, but so far it is still an unsolved problem. A
comparison of the various characteristics of Arabic, Latin,
Hebrew and Hindi scripts are outlined in Table 2.1. Arabic is
written from right to left. Arabic text (machine printed or
handwritten) is cursive in general and Arabic letters are
normally connected on the base line. This feature of
connectivity is important to be highlighted in the segmentation
process. Some machine printed and handwritten texts are not
cursive, but most Arabic texts are, and thus it is not surprising
that the recognition rate of Arabic characters is lower than that
of disconnected characters such as printed English.
Table 2.1: Comparison of Various Scripts
Characteristics
Justification
Arabic
R-to-L
Hebrew
R-to-L
Yes
Yes
3
Latin
L-toR
No
No
5
No
No
11
Hindi
L-toR
Yes
Yes
-
Cursive
Diacritics
Number of
vowels
Letters shapes
Number of
letters
Complementary
characters
1-4
28
2
26
1
22
1
40
More
than3
-
-
-
Arabic writing is similar to English in that it uses letters (which
consist of 28 basic letters), numerals, punctuation marks, as
well as spaces and special symbols. It differs from English,
however, in its representation of vowels since Arabic utilizes
various diacritical markings. The presence and absence of
vowel diacritics indicates different meanings in what would
otherwise be the same word.
i.
. However, there are four main characteristic for Arabic
language that are:
An Arabic letter might have up to four different shapes,
depending on its relative position in the text. For instance, the
letter (‫ )ع‬has four different shapes: at the beginning of the word
(preceded by a space), in the middle of the word (no space
around it), at the end of the word (followed by a space), and in
isolation (preceded by an unconnected letter and followed by a
space). These four possibilities are represented in Table 2.2, and
the different shapes of the Arabic characters in different
positions of the word.
Different Arabic characters may have exactly the
same shape, and are distinguished from each other
only by the addition of a complementary character1.
These are normally a dot, a group of dots or a zigzag
(hamza). These may appear on, above, or below the
base line and are positioned differently, for instance,
above, below or within the confines of the character.
Figure 2.1 depicts three sets of characters, the first set
having three characters and the other set two and five
characters. Clearly, each set contains characters
which differ only by the position and/or the number
of dots or zigzag shape (hamza) associated with it. It
is worth noting that any erosion or deletion of these
complementary
characters
results
in
a
misrepresentation of the character. Hence, any
thinning algorithm needs to efficiently deal with these
dots so as not to change the identity of the character.
1 Complementary characters: a portion of a character that
is needed to complement an Arabic character
72
Figure 2.1. Arabic characters differing with dots or hamza
ii.
Arabic writing is cursive such that words are
separated by spaces. However, word can be divided
into smaller units called sub words (a portion of a
word
including
one
or
more
connected
characters).Some Arabic characters are not
connectable with the succeeding character. Therefore,
if one of these characters exists in a word, it divides
that word into two sub words. These characters
appear only at the tail of a sub word, and the
succeeding character forms the head of the next sub
word. Figure 2.2 shows four Arabic words with one,
two, and three sub words. The first word consists of
one sub word which has four letters; the second has
two sub words with two letters, respectively. The
third has four sub words with three and one letter. The
last word contains four sub words, each consisting of
only one letter.
‫ﻓﻴﺼﻞ‬
‫ﻳﺎﺳﺮ‬
‫ﻋﺒﺪاﻟﺮﺣﻤﻦ‬
‫رزاق‬
Figure 2.3: Different styles and fonts for the writing of
Arabic text.
2.2 CHARACTERS RECOGNITION
Character recognition systems can throw in greatly to the
advancement of the automation process and can improve the
interaction between man and machine in many applications,
including office automation, check verification and a large
variety of banking, business and data entry applications.
Figure 2.2: Example of Arabic sub-word
iii.
Arabic writing can be, in general, classified into
typewritten (Naskh), handwritten (Ruq’a) and artistic
(or decorative Calligraphy, Kufi, Diwani, Royal, and
Thuluth) styles as shown in Figure 2.3. Handwritten
and decorative styles usually include vertical
combinations of characters called ligatures. This
feature makes it difficult to determine the boundaries
of the characters. Furthermore, characters of the same
font have different sizes (i.e. characters may have
different widths even though the two characters have
the same font and point size). Hence, word
segmentation based on a fixed size width cannot be
applied to Arabic.
There are two recognition approaches applied to printed and
handwritten Arabic character recognition. These can be
classifies as follows. First are Holistic strategies in which the
recognition is performed on the whole representation of words
and where there is no need to identify characters individually
((Dehghan, 2001); (Khorsheed, 2003) ;(El-Hajj et al., 2005);
(Alma'adeed et al., 2004), (Souici-Meslati et al, 2004) (Snoussi
Maddouri et al, 2002)). The second are Analytical strategies in
which words are not considered as a whole, but as sequences of
small size units and the recognition is not directly performed at
word level but at an intermediate level dealing with these units(
(Altuwaijri and Bayoumi,1995); (Abuhaiba et al. , 1998);
(Fahmy and Al Ali , 2001); (El-Dabi et al., 1990); (Hashemi et
al., 1995); (Mostafa, 2004); (Nawaz, S.N, Sarfraz, M., Zidouri,
A. and Al-Khatib, 2003); (Sari, T., Souici, L. and Sellami, M,
2002)).
Character recognition systems need more than one stage to
arrive at the recognition stage. In the next section described for
that stages.
73
preprocessing that prepares a concise representation of the word
image in order to be segmented. The following techniques are
common representation process.
2.3 Arabic Characters Recognition Stages
The Arabic character recognition system can be decomposed
into a number of stages: pre-processing, representation, stroke
or character segmentation, features, and recognizer. Some
approaches do not use all of these elements but only a subset.
These stages are description in table 2.3.
•
The vertical projection method helps in detecting the white
spaces and the junction lines between the adjacent characters by
counting the black pixels in each column of the word image.
Although this method is not efficient in the handwritten script
due to the overlapping and skew problems (Zahour, 2001). It
has been used with the horizontal projection to analyze the
word image into lines, word and characters these methods are
based on the fact that the connection stroke between characters
is always of less thickness than other parts of the word. In these
methods, the vertical and horizontal projections of the image
are obtained.
Table 2.3: Components of OCR Recognition
Component Description Pre‐processing Like noise removal,
detection ,similar
Representation Like Skeletons ,contours, pixels
Segmentation segment words or sub-words
or characters , strokes or other
unit
text
The horizontal projection is defined as:
h (i) = ∑ p (i, j)
Information passed to the
recognizer like shape attribute
, pixels
Features Recognizer Vertical and horizontal Projection
And the vertical projection as:
v (j) = ∑ p (i, j)
Algorithm that identifies letters
Where i is the row number and j is the column number .P is the
pixel value. It is 0 for white pixel (or background), or 1 for
black
pixel
(or
for
ground).
First, an image is cleaned with image processing techniques. It
may be converted to a more short representation, and then
features are detected from words or characters. With the
features as input, a recognizer returns the identified text string.
The term “features” does not necessarily refer to structural or
pre-computed items, but any quantities approved the recognizer.
They may be pre-computed for use in segmentation, computed
on individual letters after segmentation, or both as show in
figure 2.4 .
Figure 2.5 shows the horizontal and vertical projection profiles
of an Arabic sentence after removing the secondaries. The
longest spike in Figure 2.4(c) represents the baseline. The
thickness of baseline is resolute by computing the thickness of
the longest spike, taking the most repeated column-height
(Timsari & Fahimi 1996), or considering the position of loops
as a reference as they are always close to the baseline (Olivier
et al.,1996). Among other basic information that can be
computed from the projection profile are the width, height and
number of connected components sub-words (AlYousefi &
Udpa 1992; Mohammed 2006). The segmentation methods that
depend in this technique discussed in section 2.4.2.
Figure 2.5: Horizontal and vertical projections
(Mohammed, 2006)
Figure 2.4: Arabic Characters Recognition stages
2.4.1
Preprocessing and Representation
•
The techniques of preprocessing stage are divided into 2 types
of techniques according to their function. First the techniques
function to produce clean and usable raw data such as: noise
reduction. Second normalization and smoothing the techniques
function to prepare the data image to be segmented such as:
vertical and horizontal projection, contour tracing and skeleton
extraction. These techniques represent the preprocessing of the
segmentation process. The segmentation approach defines its
Thinning (skeleton extraction)
The thinning operation means creating the skeleton of the
image. A skeleton is a one pixel width created by highlighting
the centerline of the word image. It helps in restoring the
essential information about the word figure 2.6 Show an
example of image thinning.( Abuhaiba et al., 1994; Amin et al.
,1996; Khorsheed & Clocksin 1999, Cowell & hussain, 2001)
claim that extracting segment.
74
The special representation of “contour of projections” was
employed by Dehghani et al. in 2001. The task was Persian
character recognition, and pre-processing included median and
mathematical morphological filtering, linearization, scaling, and
centering. Regional projection contour transformation (RPCT)
was used , so the image was projected in multiple directions
(here, horizontal and vertical), and the chaincode contour of
each projection was obtained. The contour was sampled and
features were obtained for each section using a two-dimensional
pattern, the number of active pixels, and slope and curvature.
Separate feature vectors from the contours of horizontal and
vertical projections were computed and modeled by individual
HMMs, yielding two HMMs per character. During recognition,
scores from individual classifiers were integrated to improve
performance. The size of the training and testing sets was not
provided. Recognition rates were 92.76% on the training set and
71.82% on the test set. Another segmentation method depended
on thinning discuses in section 2.
Figure 2.6.: A Word image and its skeleton
In 1994, Abuhaiba et al. proposed a set of character graph
models to recognize isolated letters (Abuhaiba et a, 1994). Each
model was a state machine with transitions corresponding to
directions of segments in the character and with additional
“fuzzy” constraints to distinguish some characters. Each letter’s
skeleton was converted to a tree structure which was matched to
a model by a rule-based recognizer. Test data was written by
four people. Recognition rates depended on tuning the models
after experiments on letters by each writer, and thinning errors
caused recognition errors.
Amin et al. also used a skeleton-based graph representation for
the recognition of single letters (1996) . Structural features
including curves were fed into a five-layer neural network. The
network was trained with 2000 characters, retrained with 528 of
the 2000, and tested with another 1000 by 10 writers. A 92%
recognition rate was obtained. Difficulties included spurious
thinned lines, incorrect curve directions, and the need to modify
rules during testing.
•
The baseline is an imaginary line to connect the characters of
the word. The baseline in the segmentation stage is usually
detected. It helps in distinguishing the strokes of the characters.
Several methods have been published for detecting the baseline.
J. Kanai et al utilized the projection profile technique to detect
the fiducially points by decoding by decoding the lowest
resolution layer of the image ( Kanai et al., 1998). Detecting the
baseline is a common step in many off-line handwritten Arabic
OCR systems and it is often an important step before the
segmentation and the feature extraction steps.
Extending the work in (Abuhaiba et al., 1994) proposed a
system for the recognition of free handwritten text in 1998 . It
used the skeleton representation and segmented sub-words into
strokes that were further segmented into “tokens”. Tokens are
single vertices representing dots or loops or sequences of
vertices. The recognizer was a “fuzzy sequential machine”
which consisted of classes to be recognized, sets of initial and
terminal states, stroke directions used for entering states, and a
function for transitioning between states. Tokens were
recognized if possible, else used to augment the recognizer.
When needed, the user interactively grouped tokens into
meaningful “token strings”. To detect lines of text, strokes from
the entire page were partitioned using a minimal spanning tree
algorithm. Another graph algorithm grouped strokes into
characters and sub-words. 13 pages by 13 writers were used for
training, and another 20 pages by 20 writers were used for
testing. Writers were asked to write in a particular style, to write
the main stroke without lifting the pen, to omit diacritics, and to
avoid generating blobs, but most did not comply with these
constraints. Sub-word and character recognition rates of 55.4%
and 51.1% were obtained. No lexicon was used. In addition to
the technical method, this publication is important since it
generalized the domain to free handwriting.
•
baseline Detection
For instance, El-Hajj et al. confirmed the benefit of features
based on upper and lower baselines, within the context of
frame-based features with an HMM recognizer , included
features measuring densities, transitions, and concavities in
zones defined by the detected baselines. The system was tested
on the IFN/ENIT database a smaller amount that has fewer than
eight images. For each of four experiments, the system was
trained on three of the four image sets and tested on the
remaining set. In their experiments, the addition of the baselinedependent features to similar measurements that do not use
those
zones
significantly
improved
recognition.
2.4.2
Segmentation
The segmentation phase is a compulsory step in recognizing
printed Arabic text. Any error in segmenting the basic shape of
Arabic characters will produce a different representation of the
character component. One of the recognition strategies need the
segmentation stage which is the analytical strategies as
discussed in section 2.3. These strategies are sub classed into
two techniques have been applied for segmenting machine
printed and handwritten Arabic words into individual
characters: implicit and explicit segmentations.
Contour Tracing
In the implicit segmentation also called internal segmentation,
words are segmented into letters and recognized
simultaneously. This type of segmentation is usually designed
with rules that attempt to identify all the character’s
segmentation points. Many rules must be constructed manually
to achieve good accuracy. So the higher the number of rules, the
higher the recognition.
The tracing of the contour aims at transforming the border of
the word into a string of codes to extract the features of the
image. The coding scheme starts by identifying the position of
an initial pixel and continues identifying the relative positions
of the successive pixels on the contour until reaching the
starting pixel. The Freeman chain code is widely used as a
scheme for features extraction which to be employed either for
the segmentation process or for the recognition process.
In the explicit segmentation or external segmentation words are
externally segmented into pseudo-letters which are then
recognized individually. This approach is usually more
75
expensive due to the increased complexity of finding optimum
word hypotheses.
In character recognition, the essential information about a shape
is stored in its skeleton (Abuhaiba et al. ,1994; Khorsheed &
Clocksin, 1999) claimed that extracting segments from the
skeleton graph is more reliable than finding the actual
connection points in a word. In general, many algorithms have
been proposed to extract skeletons, but those specifically
designed for Arabic text are (Tellache et al. ,1993; Altuwaijri &
Bayoumi 1995; Altuwaijri & Bayoumi 1998; Cowell & Hussain
2001).
Almuallim and Yamaguchi (1987) also detected the
baseline of the thinned word. Then the words are segmented
into strokes. The extraction of a stroke is made by finding out
its start point. The search for the start point is done just around
the baseline, and then the curve is traced until a point which is
inferred to be the stroke end point is reached. An end point can
be a branch point, a cross point, a line end or a point with
sudden change in the curvature (up or down) after a horizontal
motion near the baseline. During the segmentation process, if
the current stroke is connected to the next stroke then the
difference between the y—coordinate of the connection point
and the current baseline is calculated. If it happened that this
difference was bigger than a certain threshold, then the baseline
is adjusted and given the value of the average of the coordinates
of the connection points found so far.
However, the studied in Arabic character segmentation divided
according to segmentation strategies.
2.3.2.1 Explicit segmentation
There is more studied standard in explicit strategy, and used
different method of representation in them works. There is more
techniques standard on Projection methods, on character
Skeleton or on Contour Tracing.
In the work of Zheng et al the sub-words consisting of one
character were excluded first as they do not need to be
segmented. Nevertheless, the algorithm used to exclude those
single characters was not able to detect all of them correctly.
Furthermore, it was also not explained how to count the number
of characters in each sub-word. The vertical projection is then
scanned to search for points near the baseline where they
changes from low to high values. Those points are considered
beginning of characters. Where the points of change from high
to low values are the end of characters. Then, some rules are
used to verify those potential segmentation points. The method
was only tested on non-overlapping fonts and segmentation rate
of 94% was reported (Zheng et al,2004).
In (Amin & Al-Sadoun 1992; Al-Sadoun & Amin 1995), the
authors traced the thinned word from right to left using a 3 x 3
window to identify potential points for segmentation. Then, a
binary tree is constructed and the skeleton is represented using
Freeman code (Freeman 1968). Each node of the binary tree
describes the shape of the corresponding part of the sub word.
The binary tree is smoothed to minimize the number of nodes
by eliminating the empty nodes, minimize the freeman code
string, and to eliminate or minimize any noise in the thinned
image. Finally, the binary tree is segmented into sub trees such
that each sub tree describes a character using primitives
including lines, loops, and double loops. Some rules were set to
ensure the correct boundaries of characters such as: long
horizontal segment signals the end of the current character, and
the existence of loops or a long vertical segment are regarded as
the beginning of a character. The algorithm can be applied to
any font and size of Arabic text, in addition, it can be applied to
hand printed text and permits the overlay of characters (Amin et
al. 1996), and however, due to the erosion experienced in the
image, some of the characters were not segmented properly.
The method was adopted in (Amin 2001). One advantage of this
method is that the identification of the baseline becomes
unnecessary since the sub word is described by a binary tree,
hence, saving processing time (Amin & Al-Sadoun 1992).
In other hand, Nawaz et al. and Sarfraz et al. used the vertical
projection
of
the
middle zone instead of the projection of the entire word. They
identified four text line zones, i.e. the upper, middle, baseline
and lower zones. The baseline zone is the one
with the highest density of black pixels, any zone just above the
baseline and twice the thickness of the baseline is the middle
zone. The vertical projection of the middle zone is constructed.
A fixed threshold is used for segmenting the word into
characters. Whenever the value of the vertical projection of the
middle zone is less than two third of the baseline thickness, the
area is considered as a connection area between two characters.
Any area follows the connection area with a larger value is
regarded as the start of a new character, as long as the profile is
greater than one third of the baseline. The method was designed
for the recognition of the Naskh font. It is clear that this method
may over-segment characters such as u. However, the authors
tried to resolve this problem in the recognition stage(Nawaz et
al. ,2003; Sarfraz et al 2003).
Although, in the work of Altuwaijri and Bayoumi (1995)
constructed the vertical projection for each sub-word excluding
the pixels of the baseline and secondaries. Potential
segmentation points are then determined using the minimum
projection values and verified by some rules which are designed
to avoid over-segmentation. .( Altuwaijri and Bayoumi ,1995)
Jambi (1991) constructed the vertical projection of the thinned
word where dots were removed. The start and end points of
characters are determined from the vertical projection; these
points could be actual points or just candidates. The actual start
point is determined if there is a change from 0 to non-zero in
the vertical projection, while the actual end point is determined
if there is a change from non-zero to 0. The candidate start point
is determined if there is a change from 1 to a greater value,
while the candidate end point is determined if there is a change
from a higher value to 1. Due to the different widths of Arabic
characters, it is not easy to avoid over-segmentation, however,
some inconsistencies can be detected easily such as having two
consecutive ends, but some are still difficult to be determined
such as u which has two actual and six candidate starting and
ending points. Applying this method will segment the tail of
when appear at the end of a word or in isolated form. The
method was adopted by Abandah and Khedher (2004). This
method needs further processing in the presence of vertical
The previous methods were designed to segment the Arabic
printed characters. The method developed by Fahmy et al
(2001) was devoted to segment handwritten text. The maximum
and minimum peaks are found from the vertical projection. The
word is then segmented into vertical strips (frames). The
boundaries of the strips are then defined to be the midpoints
between adjacent maximum / minimum pairs. To ensure that
the frames are of proper widths, the very short ones are
eliminated and the long ones are divided into shorter and the
separation point is chosen to be a portion of the character
height. Then, each frame will be divided into three horizontal
areas; one below the baseline and two above, from which
features will be extracted. However, the results are not perfect;
for instance, a character such as ‫ ط‬is divided into two frames(
Fahmy et al ,2001)
76
overlaps.
Although, in (Mostafa ,2004) proposed an adaptive rulebased segmentation algorithm based on the general structural
relationship of the Arabic text. The main rule used is that In
most characters start with, and end before a T—junction on the
baseline”. A T— junction occurs when the drawing of the
character goes up or down the baseline. This holds for all
character shapes at the middle and the end of a word, however,
few characters such as u and ‫ س‬have more than one T—
junction with the baseline, and should need a special treatment.
Structural features such as strokes, dots, loops, curves, character
relative width and height, and baseline relative position are
extracted from the skeleton using some rules. Finally, the
characters are segmented by grouping its components, e.g.
loops with their bulges like ‫ـﺼـ‬. Dots are used to help line the
grouping process. The method is noise-independent, Omni-font
and Omni-size. However, the method was tested on Simplified
Arabic
font
only
and
the
reported
segmentation accuracy was 96.5%.
segmentation rate was 86%. The algorithm suffers from oversegmentation in cases of characters like ‫ س‬and from undersegmentation in cases of ligatures. The authors claimed that
their method does not need slant correction.
Mostafa and Darwish (1999) traced the upper contour of
handwritten words searching for local minima, and at the same
time traced the lower contour searching for local maxima. The
determination of local minima and maxima are based on the
negative and positive slopes. These points are marked as
potential segmentation points. A matching process between
upper and lower potential segmentation points is
performed in order to obtain the minimum number of nonoverlapping
potential
segmentation points for each word. The algorithm achieved
97.7%
correct
segmentation. Among the advantages of this method, as
reported
by
the
authors,
are:
that it does not require the existence of a single baseline for the
whole line or even for parts of the word; it is a writer
independent and does not require any learning
procedures. Also, the problems of segmenting overlapping and
overhanging
characters
are
completely
surmounted.
Furthermore, it can effectively split touching characters.
Kandil and El-Bialy (2004) observed that the
connection strokes are formed of two parallel lines. Hence, the
contour is traced searching for this phenomenon. However, not
only the connection strokes are formed of two parallel lines but
also some other parts of the word. To overcome this problem,
only columns having the two pixels in predefined middle zone
are considered. The authors claimed that the method works for
multiple font and size and can tolerate some skewness in the
line.
Recently, (Zidouri et al.,2005) scanned the skeleton (with no
secondaries) from right to left to find a band of horizontal pixels
having length greater than or equal to the width of the smallest
character. Then the vertical projection is found, if no pixel is
encountered, a vertical line is drawn as a guide for
segmentation. The procedure is repeated for all rows. As a
result, an image with several guide bands is obtained. Features
are extracted from each guide band, a set of rules is then
designed to select from and correct the guide bands. However,
the method suffers form the problem of overlapping and
ligatures which is left to the recognition phase to deal with.
Among the drawbacks of the methods based on the
extracted skeleton, is that different thinning algorithms may
produce different thinned characters. Moreover, the thinning
process might alter the shape of the character, especially in the
case of poor quality characters. Some of the common problems
encountered during the thinning process include the elimination
of vertical notches in some characters and elimination of
secondary characters. These modificatios make the
segmentation
of
thinned
characters a difficult task. This conclusion is in agreement with
Amin (2001) and Cowell and Hussain (2001).
Safabakhsh and Adibi applied a continuous-density variableduration hidden Markov model to the recognition of
handwritten Persian words in the Nastaaligh style (2005). This
style contains many vertically overlapping letters and slop letter
sequences, which present problems for the ordering of
characters and for baseline detection. Their system removed
ascenders and descenders before the primary recognition stage
to avoid incorrect orderings and was baseline-independent.
Words were over-segmented into pseudo-characters using local
minima of their upper contour. Eight features were computed
for each pseudo-character. The HMM was path discriminate
and included 25 character states each of which was divided into
up to four sub-states to indicate position-dependent shapes. The
lexicon consisted of 50 words chosen to include all characters
and compound forms, and the training set contained two 50word scripts from each of seven writers. On a test set of two 50word scripts from two different writers and omitting words that
showed error in an earlier stage of the method, the system
achieved a 69% recognition rate with 5 iterations of the
recognition step and a 91% rate with 20 iterations. The rates
were 52.38% and 90.48% on 21 words not in the lexicon.
Methods based on contour tracing avoid all problems
resulted from the thinning process because it analyzes the
structural shape of characters as they have been scanned.
However, they are affected by noise on the contour, hence the
contour need to be smoothed first.
The set of boundary pixels or the contour includes important
information of an object (Khorsheed 2002). Segmentation is
also achieved by tracing the outer contour of a given word. The
segmentation method used in the SARAT system
(Segmentation And Recognition of Arabic printed Text)
(Margner 1992) was based on the outer contour of the main
body of the words. First, the start and the end points of the
upper contour are determined. Then, a segmentation of the
upper contour into parts is made having a curvature of the same
sign. Starting with a positive curvature, for example, the change
to a negative curvature will finish this segment and start with a
new one. In another word, wherever the outer contour changes
sign a character is segmented.
Sari et al. (2002) proposed a method known as ACSA (Arabic
Character Segmentation Algorithm) to segment Arabic
handwritten text by detecting the local minima of the lower
contour. The baseline is detected first, then, the sub-words and
secondaries are extracted using the contour tracing. Using
horizontal projection, three zones are determined for each subword; namely, upper, median and lower zones. Topological
features such as turning points, holes, zigzag, ascenders and
descenders are extracted. The segmentation point is defined as a
local minimum in the lower outer contour. A set of rules is
designed to validate the segmentation points. The reported
2.3.2.2 Implicit segmentation
Recognition-based segmentation methods dissimilar the
previously discussed methods which were considered as explicit
segmentation methods, the recognition- standard techniques is
an implicit one. In the implicit methods, characters are
77
segmented while being recognized. Hence, it is also called
recognition Based segmentation or straight-segmentation. The
basic principle of this approach is to use a mobile window of
variable width to provide the tentative segmentations which are
confirmed (or not) by the classification (Cheung et al. 2001). In
other words, the system scans the image for components that
match classes in its alphabets (Khedher & Abandah 2002).
online, and one offline recognition, and two of there in Arabic
character recognition.
2.4.1
Gildas Menier 2008: A GENETIC
ALGORITHM FOR ON-LINE CURSIVE
HANDWRITING RECOGNITION
In (El-Dabi et al. 1990), the invariant moments are calculated
and checked against the feature space of the font. If a character
is not found, another column is appended to the underlying
portion of the word and moments are calculated and checked
again. This process is repeated until a character is recognized or
the end of the word is reached. However, as the system is not
always able to recognize all characters, which implied that all
succeeding characters in that sub word would not be processed,
backup scanning algorithm is triggered when such a blockage
happened (Khorsheed 2002). To accelerate the recognition
process the scanning can be done from both ends (Khorsheed &
Clocksin 2000). The method allowed the system to handle
overlapping and to isolate the connecting baseline between
connected characters. This method seems to be limited to the
recognition of typewritten fonts; furthermore, it is font
dependent and sensitive to pattern variations. Also, the system
uses intensive computations to compute the required
accumulative moments. No figures are reported regarding the
system recognition rate and efficiency (Abuhaiba et al. 1994;
Abuhaiba 2003b). In (Auda & Raafat 1993) a similar approach
is used in which slices are added to a window and a feature
vector is fed into neural networks trying to recognize the
character first before the segmentation. The reported
segmentation
rate
was
83%.
In (Zidouri et al. 2003; Zidouri 2004), the Minimum
Covering Run (MCR) expression is used to represent the
character by a number of strokes. MCR of a region of a binary
image is the minimal combination of the run-length encoding in
both horizontal and vertical directions. The features of those
strokes are used to build reference prototypes for recognition by
matching. The separation of words into characters is done
automatically once characters composing parts are successfully
identified and a correct match is found.
This approach presents a genetic algorithm for on-line
recognition of cursive handwriting. The GAs works with a
population of solutions that called strings. Each string has a
lexical – picture and graphic primitive list which described how
the word is written. Each string is made with construction blocs
called allograph. The GAs is used to find the best reconstruction
of the word to be analyzed, based on graphic primitives and
using the allograph list. It can be seen as an alternative analysis
method for word recognition which does not require the
definition of a scanning strategy. This system achieves 84%
recognition in a manuscript test with a lexical set of 150 words
and with a small allograph set. The recognition subset is of 160
words, included ten extra words not belonging to the lexicon.
2.4.2
Ramin Halavati 2006: EVOLUTION OF
MULTIPLE STATES MACHINES FOR
RECOGNITION OF ONLINE CURSIVE
HANDWRITING
this paper presents a novel Multiple States Machine as a general
tool for elastic pattern recognition and use an evolutionary
approach to create these machines. The major idea behind the
machines is to develop and maintain different hypotheses about
the given sequence of segments and gradually prove or prune
them to reach a single final decision. It is implemented on
Persian (Farsi) language using a typical feature set and a
specific tailored genetic algorithm and the recognition
and computation time is compared with dynamic programming
comparison approach. The approach is tested over a set of
Persian language test cases with 89% best recognition rate
without dictionary and 96.1% with dictionary. It is also
compared with pruned dynamic programming, showing an
almost constant recognition speed while DP's computational
time increases exponentially when the number of segments
increase, resulting in more than 10 times faster results for 9
segment words and 100 times fast results for words with 13
segments.
As can be noticed from the above discussion, the recognitionbased segmentation approach aims at overcoming the classical
segmentation serious problems. Hence, no accurate character
segmentation path is necessary. In principle, any of the other
approaches can be used here as far as it has some recognition
capabilities (Cheung et al. 2001)
2.4
Genetic algorithm & handwriting
GAs are a class of optimization and search methods that use
randomness to avoid local extreme solutions. They are capable
of adaptive and robust search over a wide range of space
topologies. GAs were envisaged by Holland (1975) in the 1970s
as an algorithmic concept based on a Darwinian theory
‘‘survival of the fittest’’ with sexual reproduction, where
stronger individuals in the population have a higher chance of
creating an offspring. GAs are distinguished from other
techniques by a principal characteristic: they search intrinsically
parallel fashion from several solutions and not from a single
solution (Pernkopf and Bouchaffra, 2005; Schneider et al.,
2005). GA is too an iterative algorithm that depends on the
generation-by-generation development of possible solutions,
with selection schemes permitting the elimination of bad
solutions and the replication of good ones that can be modified.
2.4.3 shashank mathur 2008: OFFLINE
HANDWRITING RECOGNITION USING
GENETIC ALGORITHM
The handwriting recognition model described here works at
three stages, segmentation of the handwritten text, recognition
of segmented characters with the help of artificial neural
networks and lastly selecting the best solution from the four
artificial neural network outputs with the help of genetic
algorithm.
A robust algorithm for handwriting segmentation has been
described here with the help of which individual characters can
be segmented from a word selected from a paragraph of
handwritten text image which is given as input to the module.
Then each of the segmented characters are converted into
column vectors of 625 values that are later fed into the
advanced neural network setup that has been designed in the
Now we will take a look at the works that used the GAs in the
handwriting recognition we find five works four of there in
78
In other hand, the problem of recognition the Arabic characters
stile the interested area in a lot of studies where used different
methods to solved that problem. El-Hajj 2005 used HMMs with
four states. Khorsheed 2003 used One HMM from 32 character
he used HMMs with unlimited jumps. Other researchers used
the neural network to solve that problem like Fahmy and Al-Ali
(2001) and Souici-Meslati 2004. Other researchers detected
special roles to solve that problem. In Abuhaiba 1993-1998
their rules to match tree structures to graph models, in El-Dabi
et al. 1990 their rules of portion the word and Calculated the
invariant moments, and in Nawaz & Sarfraz(2003) Identified 4
text line zones: the upper, middle, baseline and lower zone .
form of text files. The networks has been designed with
quadruple layered neural network with 625 input and 26 output
neurons each corresponding to a character from a-z, the outputs
of all the four networks is fed into the genetic algorithm which
has been developed using the concepts of correlation, with the
help of this the overall network is optimized with the help of
genetic algorithm. The algorithms were tested with 200
handwritten samples out of which 142 samples were correctly
recognized providing with an overall efficiency of 71.0%
2.4.4
Kherallah and et al.: On-line Arabic
handwriting recognition system based on visual
encoding and genetic algorithm
Howevere, some researchers used GAs to recognized Arabic
characters. Alimi 1997 used Fuzzy Neural Network and GAs to
select the best combination of characters recognized by a fuzzy
neural network. Where Kherallah and et al 2008 used visual
encoding and GAs in on-line Arabic handwriting. Although,
this work discus a lot of works in representation the image of
Arabic word, segment it and recognize it.
In this approach, a GAs has been developed in order to select
the best combination of visual codes extracted from a word by
the heuristic method. The evolutionary approach here permits
the recognition of cursive handwriting without the limitation of
a lexical dictionary. It has been known that there is no
guarantee that global optimization can always be found by
using (GAs). Therefore, the convergence of GAs algorithm is
assured by the technique given in the fitness function which
consists in the use of the visual codes of Arabic words and the
comparison method established between the visual indices
strings. The number of generations (500) and the fitness value
(0.5) are fixed as a convergence condition criterion. The
average of the recognition rate found is about 97%.
4.
CONCLUSION AND SUGGESTED
WORK
In this paper a comprehensive review in the literature review in
the stage of Arabic character recognition. It is concluded that
the data bases that the researcher used were a small data with
limited number of the words. So this area needs a large data
base with different types of words and paragraph. Therefore this
area of research is still open for further enhancement. Extensive
research need to be conducted.
2.4.5
Alimi: Evolutionary Neuro-Fuzzy
Approach to Recognize On-Line Arabic
Handwriting
5.
Alimi set forth a complete system that segmented letters
according to an understanding of the way that humans write.
Given that an Arabic letter can have at most 6 strokes and that a
stroke is defined as an asymmetric bell-shaped function of
curvilinear velocity with the speed tapering off at the end of the
stroke, a system can automatically segment a letter into substrokes, which define that letter. Each character can be
represented as 6 feature vectors. If the character has less than 6
strokes, the empty strokes are zeroed out.
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