An Ensemble Learning Approach Using Decision Fusion For The Recognition Of Arabic Handwritten Characters

An Ensemble Learning Approach using Decision Fusion for the
Recognition of Arabic Handwritten Characters
Rihab Dhief1
, Rabaa Youssef1,2
and Amel Benazza1
1University of Carthage SUP’COM, LR11TIC01, COSIM Lab., 2083, El Ghazala, Tunisia
2INSAT, University of Carthage, Tunisia
Keywords: Handwritten Arabic Character Recognition, Skeletonization, Freeman Chain Code, Heutte Descriptors,
Feature Extraction, Supervised Machine Learning Algorithms, Deep Learning.
Abstract: The Arabic handwritten character recognition is a research challenge due to the complexity and variability of
forms and writing styles of the Arabic alphabet. The current work focuses not only on reducing the complexity
of the feature extraction step but also on improving the Arabic characters’ classification rate. First, we lighten
the preprocessing step by using a grayscale skeletonization technique easily adjustable to image noise and
contrast. It is then used to extract structural features such as Freeman chain code and Heutte descriptors.
Second, a new model using the fusion of results from machine learning algorithms is built and tested on
two grayscale images’ datasets: IFHCDB and AIA9K. The proposed approach is compared to state-of-the-art
methods based on deep learning architecture and highlights a promising performance by achieving an accuracy
of 97.97% and 92.91% respectively on IFHCDB and AIA9K datasets, which outperforms the classic machine
learning algorithms and the deep neural network chosen architectures.
1 INTRODUCTION
Arabic is an international language widely spoken in
the world. The Arabic alphabetic contains 28 letters
written from right to left and they are highly similar to
each other. The Arabic language has flourished in sev-
eral eras, and gained a scientific, literary and religious
weight. Digitizing this heritage is very important for
better archiving and exploration.
Optical Character Recognition (OCR)
(Borovikov, 2014) is the process of converting
images of handwritten or printed text into digital,
machine-editable text. Despite the attention given
to the optical character recognition field and the
interesting results of the literature (Alaei et al., 2012;
Rajabi et al., 2012; Siddhu et al., 2019; Althobaiti
and Lu, 2017; Altwaijry and Al-Turaiki, 2021;
Boulid et al., 2017; KO and Poruran, 2020; Balaha
et al., 2021b), the recognition of Arabic handwritten
characters still has its challenges and difficulties. In
fact, its cursive writing style generates a variation
in shape, curve angles and size of each character,
depending on its position in the word. Furthermore,
various characters in the Arabic alphabet have the
same main body but can be differentiated only
by the position and number of the diacritics (Lutf
et al., 2010). Many Arabic datasets are provided by
open-access resources. Some datasets include only
simplified binary characters (El-Sawy et al., 2017;
Altwaijry and Al-Turaiki, 2021), while some others
gather grayscale images (Torki et al., 2014; Mozaffari
et al., 2006).
In this work, we propose to investigate a new ar-
chitecture that brings together many algorithms in or-
der to take advantages of each of them. First, we pro-
pose to combine structural and statistical features. To
obtain the structural features, the Self-Noise and Con-
trast Controlled Thinning algorithm (Youssef et al.,
2016) is implemented. This algorithm lighten the pre-
processing step by improving the model robustness to
noise and low-contrast. In fact, the SCCT algorithm
has proven its efficiency in the medical field (Mallat
and Youssef, 2016), when directly applied on X-Ray
images. Second, the principle contribution of the cur-
rent work consists of implementing a decision fusion
based on the most efficient machine learning classi-
fiers, namely SVM, KNN and RF.
The structure of this paper is described as follows.
First, Section 2 reviews the pertinent works in Ara-
bic handwritten recognition. Second, Section 3 de-
scribes the datasets used in this work. Then, Sec-
tion 4 highlights the feature extraction step, using the
SCCT skeletonization. Section 5 details the proposed
model: the fusion of machine learning classifiers. Ex-
perimental results are showed in Section 6. Finally,
conclusions are drawn in Section 7.
Dhief, R., Youssef, R. and Benazza, A.
An Ensemble Learning Approach using Decision Fusion for the Recognition of Arabic Handwritten Characters.
DOI: 10.5220/0010839500003122
In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 51-59
ISBN: 978-989-758-549-4; ISSN: 2184-4313
Copyright c 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
51

2 RELATED WORK
For years, the problem of classification of Arabic
characters has existed, several studies have been car-
ried out to find as much precision as possible. Three
important steps in the recognition system should be
treated : Preprocessing , Feature Extraction and Clas-
sification.
2.1 Preprocessing
Preprocessing aims at removing unnecessary infor-
mation without modifying the form of the object.
This is a fundamental step to ensure good classifica-
tion results. Traditional preprocessing methods are
generally filtering and noise removal (Althobaiti and
Lu, 2017; Sahlol et al., 2014). Thining and Ob-
ject contour are also often used techniques especially
for identifying the object structure (Boufenar et al.,
2018). These techniques are efficient in the character
recognition field but since they are based on binary
image, they still risk losing useful information during
the binarization step.
2.2 Feature Extraction
Feature engineering is a primordial step for every
ML learning model. It consists of transforming im-
age data into features that better represent the under-
lying problem to the predictive models, resulting in
improved model accuracy on unseen data. Recently,
many researchers use deep features for the feature
extraction step. This method does not need further
preprocessing and achieves high performance accord-
ing to the literature (Altwaijry and Al-Turaiki, 2021;
Balaha et al., 2021a). Despite its effectiveness, it re-
quires a higher computational costs than traditional
methods. Traditional feature extraction is performed
through two main shape description approaches: sta-
tistical and structural. Studies based on supervised
learning using only structural features (Althobaiti and
Lu, 2017), or statistical approaches (Alaei et al., 2012;
Rajabi et al., 2012), and also the combination of both
(Alaei et al., 2012; Zanchettin et al., 2012; Sahlol
et al., 2014; Sahlol et al., 2016; Boufenar et al.,
2018; Siddhu et al., 2019) were proposed in the litera-
ture with the latter achieving more interesting results.
Combining both type of features explains the need
for merging different classifiers since each classifier
needs different form of features for better results.
2.3 Classification
Previous works propose two main streams of ap-
proaches to deal with the Arabic handwritten recog-
nition problem, namely Deep Neural Network (DNN)
architectures (Altwaijry and Al-Turaiki, 2021; KO
and Poruran, 2020; Balaha et al., 2021a) and classi-
cal Machine Learning (ML) techniques (Zanchettin
et al., 2012; Alaei et al., 2012; Rajabi et al., 2012;
Sahlol et al., 2014; Sahlol et al., 2016; Boufenar
et al., 2018; Siddhu et al., 2019; Ali et al., 2020).
On the one hand, and regarding the use of Deep Neu-
ral Networks, authors of (Altwaijry and Al-Turaiki,
2021) propose a convolutional neural network ap-
proach (CNN) for the recognition of Arabic hand-
written characters using a small binary dataset and
achieving an accuracy result of 97%, while authors
of (Boulid et al., 2017; KO and Poruran, 2020; Bal-
aha et al., 2021a) conduct a research on grayscale
character images with a result of 96%. On the other
hand, and regarding the use of classical Machine
Learning (ML) approaches, three main models have
been widely applied, namely Support Vector Ma-
chine (SVM) (Zanchettin et al., 2012; Alaei et al.,
2012; Rajabi et al., 2012; Sahlol et al., 2014; Siddhu
et al., 2019), Random Forest (RF) (Sahlol et al., 2016;
Rashad and Semary, 2014) and K-Nearest Neighbors
(KNN) (Zanchettin et al., 2012; Rajabi et al., 2012;
Sahlol et al., 2014; Boufenar et al., 2018), with a
highlight on SVM results in most of the cited exper-
imental results. Furthermore, the idea of combining
multiple classifiers emerged in the past few years us-
ing both DL (Bosowski et al., 2021) and ML algo-
rithms (Zhao and Liu, 2020; Kaoudja et al., 2019).
Ensemble learning is an efficient way to take advan-
tage of different classifiers especially when they have
heterogeneous inputs (different features type). In fact,
the authors of (Zanchettin et al., 2012) combine the
SVM and KNN classifiers, also, RF and KNN com-
bination is proposed in (Zhao and Liu, 2020) for
numeral recognition, and multi-classifier system for
Arabic calligraphy recognition is built in (Kaoudja
et al., 2019), merging three classifiers namely: Mul-
tilayer Perceptron (MLP), SVM, and KNN. Although
state-of-the-art methods for recognising binary Arab
handwritten characters have achieved satisfactory re-
sults(Alaei et al., 2012; Rajabi et al., 2012; Zanchettin
et al., 2012; Sahlol et al., 2014; Zhao and Liu, 2020;
Kaoudja et al., 2019), further improvements remain
conceivable by adopting new approaches and method-
ologies on grayscale image datasets.
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
52

3 DATASETS
Two datsets are used in the curret work, namely
Isolated Farsi Handwritten Character Data Base
(IFHCDB) and AlexU Isolated Alphabet (AIA9K).
Both datasets contain grayscale images.
3.1 The IFHCDB Dataset
The IFHCDB dataset (Mozaffari et al., 2006) in-
cludes isolated Farsi and Arabic handwritten charac-
ters. In our project, only Arabic letters are considered.
The Arabic alphabet contains 28 letters and thus, our
model contains 28 classes. The main issue with this
dataset is the lack of balance between the classes as-
sociated to each character. In total, IFHCDB dataset
contains 51029 Arabic character with different num-
ber of observation varies from 40 to 10000 per class.
To reduce this unbalance, we merge classes based on
same character body and variable diacritics. We end
up with 18 classes presented in Figure 1.
Figure 1: The character bodies.
3.2 The AIA9K Dataset
The total size of AIA9K dataset (Torki et al., 2014) is
8736 divided in 28 classes with number of observa-
tions varies from 251 and 278 per class. For sake of
clarity, we merged classes with same character body,
as for IFHCDB dataset.
4 HANDCRAFTED FEATURE
EXTRACTION
Two types of features are extracted: structural ones
from the skeleton and statistical ones from the char-
acter body. Heutte et al. (Heutte et al., 1998) col-
lected a set of statistical and structural features that
describes the character globally (projection, moments
and profiles) and locally (Intersection with straight
lines, holes, concave arcs, junctions, endpoints and
extrema). These descriptors browse almost all what
could characterize a character. We briefly describe
these features in the next two subsections.
4.1 Features Extracted from the
Character Body
Hu Moments: The seven equations are detailed in
(Hu, 1962) and are invariant to position, size and ori-
entation of the character.
Projections: Vertical and horizontal projections de-
rived from the histograms of the character image are
calculated and the maxima from each is extracted to
locate the most significant object pixels number verti-
cally and horizontally.
Profiles: The profile correspond to the set of differ-
ences between two consecutive pixels (between two
ordinates in the right and left profiles or two abscissas
otherwise). The profiles provide information about
the harmony of the character.
Heights and Widths: They describe the character
in terms of height (respectively width) in particular lo-
cations (1/5, 1/2 and 4/5). They are extracted from the
difference between the left and the right raw profiles
(respectively bottom and top) of the character bound-
ing box.
Extrema: Top, bottom, left and right extrema of the
character are extracted by browsing the image from
left to right, right to left, top to bottom and bottom to
top. Each time, the first pixel which does not have an
8-connected object neighbour is recorded.
Concave Arcs: They are extracted from the object
contours. A concave arc is the set of three consecutive
points that form an angle of less than 180 degrees.
Ratio: The height to width ratio is extracted and
aims at characterizing the spread of the character.
4.2 Features Extracted from the
Skeleton
Self-Noise and Contrast Controlled Thinning (SCCT)
was developed by the authors of (Youssef et al.,
2016). It generates a smooth silhouette using two
thinning parameters: contrast and noise. This study
proposes to relax the topology preservation prop-
erty of homotopic thinning by considering local noise
and contrast, as shown by Figure 2. Applying this
skeletonization method improves the skeleton’s qual-
ity compared to binary skeletonization (Zhang and
An Ensemble Learning Approach using Decision Fusion for the Recognition of Arabic Handwritten Characters
53

(a) (b) (c)
(a) (b) (c)
Figure 2: Comparison:(a) Character body, (b) Binary thin-
ning, (c) Grayscale thining.
Suen, 1984) and consequently, the final classification
results.
Figure 2 presents two examples from class 14 and
6. The binary skeletonization (Zhang and Suen, 1984)
fails at recognizing the character’s hole for the letter
”sad” while creating one in the wrong place for letter
”ha”. This confusion is problematic since hole detec-
tion remains an essential feature to separate classes.
Endpoints and Junctions: An endpoint is an ob-
ject pixel that has only one 8-connected object neigh-
bor. A junction is defined as a pixel having at least
three 8-connected object neighbors that separate the
background into three or more 4-connected compo-
nents.
Holes and Intersections with Straight Lines: A
hole is detected when the image background contains
more than one 4-connected component. The intersec-
tions extraction are defined as follows: two horizontal
lines (1/3 height and 2/3 height) and a vertical line
crossing the character’s centre of gravity.
Freeman Chain Code: Freeman’s chain converts a
skeleton image into a directional code. Our process
of extracting the Freeman chain code is described as
follows: the starting point is the first encountered end-
point pixel. Then, based on the position of the current
pixel neighbour, we pick the appropriate Freeman di-
rection to start constructing the chain code. Figure
3 (a) states the common choice of the 8 directions.
An example in Figure 3 (b) details the Freeman chain
code constructed for letter ”Lam”.
(a)
(b)
Figure 3: (a) Freeman 8 directions, (b) The chain code of
the letter ”lam”.
4.3 Features Extracted from Diacritics
Diacritics are obtained after removing the character
body from the image. The number, size and position
of each diacritic are extracted to differentiate between
characters having similar bodies but differing in di-
acritics. Figure 4 shows some example of diacritics
encountered in the AIA9K dataset.
Figure 4: Different types of diacritics.
5 THE PROPOSED
METHODOLOGY: ENSEMBLE
LEARNING CLASSIFIER
USING DECISION FUSION
Once these statistical and morphological features are
computed, they should undergo a classification in or-
der to recognize the underlying character. In this re-
spect, we adopt a decision fusion strategy based on
the most efficient machine learning classifiers used
in the character recognition field, namely weighted
SVM, weighted RF and K-NN (Ayodele, 2010).
5.1 The Classifiers
Weighted Support Vector Machine: SVM is ini-
tially used for binary classification. It consists of
defining a hyperplane that separates two classes.
As we are faced to a multi-classification problem
with m > 2 classes, the strategy one versus one is
adopted in order to apply m(m − 1)/2 binary classi-
fiers. Weighted SVM is adopted in our work because
of the datasets unbalance.
Weighted Random Forest: Random forest consists
of a set of decision trees. Every decision tree gives a
54

Figure 5: The proposed architecture for Arabic handwritten characters recognition.
predicted class. Then, the most frequently predicted
classes is chosen. RF and SVM are used to classify
characters using numeric statistical and structural fea-
tures.
K-Nearest Neighbors: K-NN is essentially based
on the calculation of metrics (distance) between ob-
servations. For each new observation, we can pre-
dict its class by looking at the classes of its nearest
neighbors. The number of considered neighbors in
the K-NN classifier is the parameter K which is set
empirically from the beginning. The K-NN classifies
the Freeman chains by using the Levenshtein distance
(Levenshtein et al., 1966), which is a string metric for
measuring the difference between two sequences.
5.2 Ensemble Learning: Decision-fusion
Principle
In this section, the suggested methodology for the
recognition of Arabic handwritten characters is pre-
sented. Figure 5 illustrates the proposed system in
a block diagram. First, a preprocessing step is pre-
sented, where two information sources of the char-
acter are described: the binary character itself and
its skeleton graph. The aim of this work is to test
the SCCT method, which makes it possible to avoid
part of the pretreatments, and to see its effect on the
classification rate.The second step consists in extract-
ing features from each character form. Structural fea-
tures are extracted from the skeleton, while statistical
ones are derived from the character body. Besides, the
number, position and size are also calculated for the
diacritics.
The final step is the classification, which is imple-
mented in two main steps :
1. First stage: The character body classification: As
described in subsection 5.1, three main classifiers
are used, namely SVM, RF, K-NN and for which
we choose different features as input. In fact,
K-NN uses only the Freeman chain code feature
since this chain requires the use of a specific met-
ric which is here the Levenshtein distance. Re-
garding SVM and RF, we choose to implement
them using the remaining features described in
this work. Since the classification error is differ-
ent from a classifier to another, a comparison be-
tween their results is made, and a vote that merges
the respective decisions is built. In fact, we choose
the most common predicted class between the
three classification results. If the three predicted
classes are different, the class corresponding to
the best global accuracy is picked.
2. Second stage: Separate merged classes using the
diacritics: in fact, due to the similarity between
classes and the notable unbalance of the datasets,
merging classes having the same body but differ-
ing by the diacritics’ forms was adopted to im-
prove the global accuracy. In this step, we sepa-
rate the merged classes using information related
to the diacritics. Each character is classified by
adding the features related to its diacritics, and in
this case, the SVM classifier is used since it gen-
erates better results when dealing with unbalanced
data.
55

Table 1: Comparison between binary and grayscale methods using IFHCDB dataset.
SCCT Thome Zhang
93.6% 92.9% 93.3%
Table 2: Parameter tuning for the three classifiers.
Weighted SVM Weighted RF KNN
Kernel = Linear Max−depth = 50 Distance=Levenshtein(Levenshtein
et al., 1966)
IFHCDB C = 16 Number of estimators = 900 K = 5
AIA9K C=12 Number of estimators = 900 K = 7
6 EXPERIMENTAL RESULTS
Our contribution lies in the use of the SCCT skele-
tonization and the decision fusion of the three classi-
fiers. Thus, in this section, we evaluate the results of
the two contributions separately.
6.1 SCCT Contribution
In this part, a comparison between binary and
grayscale skeletonization is made on the IFHCDB
database. Since the Freeman chain code is the de-
scriptor that fully exploits the skeleton: its shape,
endpoints and junctions, we choose to use it for test-
ing the efficiency of the SCCT skeletonization and
thus to implement the K-NN classifier. Alongside the
SCCT, two binary methods are used in the compar-
ison : Thome skeletonization (Merad et al., 2010)
and Zhang skeletonization (Zhang and Suen, 1984).
The adjustment of the SCCT parameters regarding the
contrast and noise of the image aims at finding a com-
promise between preserving the topology of the ob-
ject and removing noise related information. For this
purpose, the two following parameters must be set in
the SCCT skeletonization technique:
• The standard deviation of the background noise:
Since all the images in the dataset are acquired un-
der the same conditions, we can precalculate noise
standard deviation empirically by choosing a re-
gion from a set of image background, on which
we calculate the standard deviation.
• Test confidence level: This parameter is inti-
mately linked to contrast level. According to the
authors (Youssef et al., 2016), and for images that
are correctly contrasted, a confidence level of 0.01
is adequate.
According to Table 1, the results provided by
SCCT skeletonization comfort us in our choice, since
we can remove all preprocessing steps and, at the
same time, improve classification results. By doing
so, the risk of damaging significant information is re-
duced. These results support our first observations in
Figure 2.
6.2 Classifiers Decision Fusion
Contribution
Parameter Tuning: Cross-validation was used to
compare the performance of different predictive mod-
els: weighted SVM, weighted RF and K-NN. A 5-
fold cross-validation was conducted on the training
dataset in order to choose the best parameters config-
uration. Table 2 details the chosen parameters values
for each model on respective dataset. All parameters
were chosen empirically.
Results: The global accuracy for the three clas-
sifiers are presented in Table 3. Concerning the
IFHCDB dataset, we need to further merge class ”ba”
and ”fa” since we notice an important confusion be-
tween the two classes: 18% from class ”fa” were pre-
dicted belonging to class ”ba”. In addition, we also
merge classes 2 and 15, classes 10 and 11, in the
AIA9K data for the same reason.
Globally, the IFHCDB dataset results are better than
those of the AIA9k database due to size issue.
IFHCDB dataset contains 50k images while AIA9K
presents only 8k images. According to Table 3,
weighted SVM works better with unbalanced data,
while RF gives higher results with small data. KNN
has the lowest accuracy, but provides different infor-
mation since it uses another type of feature, the ’Free-
man chain code’.
Table 3: Accuracy of different classifiers.
Models IFHCDB AIA9K
SVM 97.88% 87.56%
RF 96.92% 91.08%
KNN 94.43% 84.83%
56

Table 4: Accuracy results on IFHCDB and AIA9K dataset of separate classifiers compared to the decision fusion approach.
IFHCDB AIA9K
Class ”ha” Class ”ain” Class ”ba+noun” Class ”kef”
KNN 97% 94% 93% 62%
Weighted SVM 99% 91% 90% 79%
Weighted RF 100% 93% 88% 77%
Fusion 100% 96% 92% 89%
Table 5: Accuracy compared to the literature.
Papers Classifier Database Number of
classes
Accuracy
(Alaei et al., 2012) SVM IFHCDB 32-classes 96.91%
(Boulid et al., 2017) Neural Network IFHCDB 28-classes 96%
(KO and Poruran,
2020)
CNN IFHCDB 28-classes 96.3%
Proposed model Fusion of classifiers’
decisions
IFHCDB 28-classes 97.97%
(Balaha et al., 2021a) Deep learning sys-
tem
AIA9K 28-classes 93.3%
Proposed model Fusion of classifiers’
decisions
AIA9K 28-classes 92.91%
In fact, and according to results exposed in Ta-
ble 4 we notice that each classifier succeeds/fails in
different situations. For example, in the case of the
IFHCDB dataset, the global accuracy of K-NN is
more interesting on letter ”ain”, while RF achieves
100% on letter ”ha”. In the case of the AIA9K dataset,
same remark can be made since we notice a 10%
improvement in classification result when using fu-
sion on class ”kef”, compared to the best classifier
(Weighted SVM). This reveals that the classification
error is different from a classifier to another and thus,
support the idea of merging the decisions of the three
machine learning approaches.
The proposed decision fusion contribution has im-
proved the accuracy up to 98.74% for the IFHCDB
dataset, which is a significant result. The accuracy
rate of different classes varies from 85% to 100%.
Almost all the classes have an accuracy greater than
94%. And, a F1-score of 97.5% is obtained. For the
AIA9K dataset, the fusion result gives an accuracy of
94.58% and an F1-score of 94.5%, which are interest-
ing results regarding the limited size of the dataset.
In order to process the 28 classes, we moved to
the second stage where features related to the dia-
critics are used: number, size, and position. The
Weighted SVM classifier is implemented to separate
similar character bodies. In this step, the separation is
done with 100% of precision in most classes. How-
ever, there is an issue with very similar characters’
diacritics, such as letter ’ba’ and letter ’tha’, leading
to a small decrease in overall accuracy : 97.97% for
IFHCDB and 92.91% for AIA9K.
Comparison with State of the Art: Table 5
presents our results and the ones of previously cited
works, for instance, deep learning models (Boulid
et al., 2017; KO and Poruran, 2020) and SVM clas-
sifier of (Alaei et al., 2012). Since we performed the
classification on the same datasets as the above cited
works, we use in this comparison the results detailed
in their respective papers.
By mixing structural and statistical features ex-
tracted from both the character body and the skele-
ton, and by combining traditional classifiers to bring
out the best of each, we obtain the highest accuracy
of 97.97% among the cited methods from literature.
Another important remark is that these results are ob-
tained on highly unbalanced dataset without using any
data augmentation technique. One can also notice
that the separation of merged classes in the case of
AIA9K dataset did not benefit the overall accuracy,
since some of the separated classes has really few ob-
servations, which declined the overall accuracy.
7 CONCLUSIONS
In this paper, a method to recognize handwritten Ara-
bic character is presented. The proposed approach in-
cludes a data analysis step to extract each character’s
most accurate descriptors and a classification step. In
57

the first step, a new technique of skeletonization is
used to improve the feature extraction phase. In the
modelling step, a new classification method is pro-
posed resulting in an interesting accuracy rate com-
pared to separate classifiers and Deep Learning ar-
chitectures when tested on the IFHCDB dataset. A
data augmentation technique should be done in future
works to improve the result on AIA9K dataset due to
its small size.
REFERENCES
Alaei, A., Pal, U., and Nagabhushan, P. (2012). A com-
parative study of persian/arabic handwritten charac-
ter recognition. In 2012 International Conference
on Frontiers in Handwriting Recognition, pages 123–
128. IEEE.
Ali, A. A. A., Suresha, M., and Ahmed, H. A. M. (2020).
A survey on arabic handwritten character recognition.
SN Computer Science, 1(3):1–10.
Althobaiti, H. and Lu, C. (2017). A survey on arabic opti-
cal character recognition and an isolated handwritten
arabic character recognition algorithm using encoded
freeman chain code. In 2017 51st Annual Conference
on Information Sciences and Systems (CISS), pages 1–
6. IEEE.
Altwaijry, N. and Al-Turaiki, I. (2021). Arabic hand-
writing recognition system using convolutional neu-
ral network. Neural Computing and Applications,
33(7):2249–2261.
Ayodele, T. O. (2010). Types of machine learning algo-
rithms. New advances in machine learning, 3:19–48.
Balaha, H. M., Ali, H. A., Saraya, M., and Badawy, M.
(2021a). A new arabic handwritten character recogni-
tion deep learning system (ahcr-dls). Neural Comput-
ing and Applications, 33(11):6325–6367.
Balaha, H. M., Ali, H. A., Youssef, E. K., Elsayed, A. E.,
Samak, R. A., Abdelhaleem, M. S., Tolba, M. M.,
Shehata, M. R., Mahmoud, M. R., Abdelhameed,
M. M., et al. (2021b). Recognizing arabic handwritten
characters using deep learning and genetic algorithms.
Multimedia Tools and Applications, pages 1–37.
Borovikov, E. (2014). A survey of modern optical
character recognition techniques. arXiv preprint
arXiv:1412.4183.
Bosowski, P., Bosowska, J., and Nalepa, J. (2021). Evolving
deep ensembles for detecting covid-19 in chest x-rays.
In 2021 IEEE International Conference on Image Pro-
cessing (ICIP), pages 3772–3776. IEEE.
Boufenar, C., Batouche, M., and Schoenauer, M. (2018).
An artificial immune system for offline isolated hand-
written arabic character recognition. Evolving Sys-
tems, 9(1):25–41.
Boulid, Y., Souhar, A., and Elkettani, M. Y. (2017). Hand-
written character recognition based on the specificity
and the singularity of the arabic language. Interna-
tional Journal of Interactive Multimedia & Artificial
Intelligence, 4(4).
El-Sawy, A., Loey, M., and El-Bakry, H. (2017). Ara-
bic handwritten characters recognition using convolu-
tional neural network. WSEAS Transactions on Com-
puter Research, 5:11–19.
Heutte, L., Paquet, T., Moreau, J.-V., Lecourtier, Y., and
Olivier, C. (1998). A structural/statistical feature
based vector for handwritten character recognition.
Pattern recognition letters, 19(7):629–641.
Hu, M.-K. (1962). Visual pattern recognition by moment
invariants. IRE transactions on information theory,
8(2):179–187.
Kaoudja, Z., Kherfi, M. L., and Khaldi, B. (2019). An effi-
cient multiple-classifier system for arabic calligraphy
style recognition. In 2019 International Conference
on Networking and Advanced Systems (ICNAS), pages
1–5. IEEE.
KO, M. A. and Poruran, S. (2020). Ocr-nets: Variants of
pre-trained cnn for urdu handwritten character recog-
nition via transfer learning. Procedia Computer Sci-
ence, 171:2294–2301.
Levenshtein, V. I. et al. (1966). Binary codes capable of cor-
recting deletions, insertions, and reversals. In Soviet
physics doklady, volume 10, pages 707–710. Soviet
Union.
Lutf, M., You, X., and Li, H. (2010). Offline arabic
handwriting identification using language diacritics.
In 2010 20th International Conference on Pattern
Recognition, pages 1912–1915. IEEE.
Mallat, K. and Youssef, R. (2016). Adaptive morphologi-
cal closing based on inertia tensor for structuring ele-
ment estimation. In 2016 International Symposium on
Signal, Image, Video and Communications (ISIVC),
pages 253–258. IEEE.
Merad, D., Aziz, K.-E., and Thome, N. (2010). Fast people
counting using head detection from skeleton graph. In
2010 7th IEEE International Conference on Advanced
Video and Signal Based Surveillance, pages 233–240.
IEEE.
Mozaffari, S., Faez, K., Faradji, F., Ziaratban, M., and
Golzan, S. M. (2006). A comprehensive isolated
farsi/arabic character database for handwritten ocr re-
search. In Tenth International Workshop on Frontiers
in Handwriting Recognition. Suvisoft.
Rajabi, M., Nematbakhsh, N., and Monadjemi, S. A.
(2012). A new decision tree for recognition of per-
sian handwritten characters. International Journal of
Computer Applications, 44(6):52–58.
Rashad, M. and Semary, N. A. (2014). Isolated printed ara-
bic character recognition using knn and random forest
tree classifiers. In International Conference on Ad-
vanced Machine Learning Technologies and Applica-
tions, pages 11–17. Springer.
Sahlol, A., Abd Elfattah, M., Suen, C. Y., and Hassanien,
A. E. (2016). Particle swarm optimization with ran-
dom forests for handwritten arabic recognition sys-
tem. In International Conference on Advanced In-
telligent Systems and Informatics, pages 437–446.
Springer.
Sahlol, A. T., Suen, C. Y., Elbasyoni, M. R., and Sallam,
A. A. (2014). Investigating of preprocessing tech-
58

niques and novel features in recognition of handwrit-
ten arabic characters. In IAPR Workshop on Artificial
Neural Networks in Pattern Recognition, pages 264–
276. Springer.
Siddhu, M. K., Parvez, M. T., and Yaakob, S. N. (2019).
Combining statistical and structural approaches for
arabic handwriting recognition. In 2019 International
conference on computer and information sciences (IC-
CIS), pages 1–6. IEEE.
Torki, M., Hussein, M. E., Elsallamy, A., Fayyaz, M.,
and Yaser, S. (2014). Window-based descriptors
for arabic handwritten alphabet recognition: a com-
parative study on a novel dataset. arXiv preprint
arXiv:1411.3519.
Youssef, R., Sevestre-Ghalila, S., Ricordeau, A., and Be-
nazza, A. (2016). Self noise and contrast controlled
thinning of gray images. Pattern Recognition, 57:97–
114.
Zanchettin, C., Bezerra, B. L. D., and Azevedo, W. W.
(2012). A knn-svm hybrid model for cursive hand-
writing recognition. In The 2012 International Joint
Conference on Neural Networks (IJCNN), pages 1–8.
IEEE.
Zhang, T. Y. and Suen, C. Y. (1984). A fast parallel algo-
rithm for thinning digital patterns. Communications
of the ACM, 27(3):236–239.
Zhao, H.-h. and Liu, H. (2020). Multiple classifiers fu-
sion and cnn feature extraction for handwritten digits
recognition. Granular Computing, 5(3):411–418.
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