A Deep Convolutional and Recurrent Approach for Large
Vocabulary Arabic Word Recognition
Faten Ziadi
1,2 a
, Imen Ben Cheikh
1 b
and Mohamed Jemni
1 c
1
University of Sousse, ISITCom, 4011, Sousse, Tunisia
2
Latice Laboratory, ENSIT, University of Tunis, Tunis, Tunisia
Keywords: CNN, LSTM, Arabic Writing, Large Vocabulary, Linguistic Knowledge, APTI Dataset.
Abstract: In this paper, we propose a convolutional recurrent approach for Arabic word recognition. We handle a large
vocabulary of Arabic decomposable words, which are factored according to their roots and schemes.
Exploiting derivational morphology, we have conceived as the first step a convolutional neural network,
which classifies Arabic roots extracted from a set of word samples int the APTI database. In order to further
exploit linguistic knowledge, we have accomplished the word recognition process through a
recurrent network, especially LSTM. Thanks to its recurrence and memory cabability, the LSTM model
focuses not only prefixes, infixes and suffixes listed in chronological order, but also on the relation between
them in order to recognize word patterns and some flexional details such as, gender, number, tense, etc.
1 INTRODUCTION
Recognition is an area that covers various fields such
as facial recognition, fingerprint recognition, image
and character recognition, number recognition, etc.
For decades, considerable progress has been made in
handwriting recognition.
Our work focuses primarily on this field of
research, specifically Arabic writing recognition.
Arabic is a mother Semitic tongue (Ibrahim,
Bilmas and Babiker, 2013) spoken by hundreds of
millions of people in Middle Eastern countries.
The following figure (Figure 1) illustrates the 28
characters of the Arabic alphabet.
The morphological complexity of written Arabic
and its cursivity remains a very broad subject area
which has known in recent years a great progress in
diverse fields.
Indeed, the complexity of the recognition process
depends on the type of script (printed or handwritten),
the approach (holistic, pseudo-global or analytic)
(Touj,Ben Amara and Amiri, 2007) (Avila, 1996) and
the vocabulary size (reduced, large).
a
https://orcid.org/0000-0002-1966-5630
b
https://orcid.org/0000-0003-2119-1312
c
https://orcid.org/0000-0001-8841-5224
Figure 1: The 28 character of the Arabic alphabet.
Numerous approaches have been proposed,
dealing with letters and / or pseudo-words, and
several works have experienced statistical, neural,
stochastic methods on different types of Arabic
documents.
Among these approaches, we will focus on neural
networks by presenting two variants: convolutional
networks and recurrent networks with LSTM
memory.
In this context, we have chosen to combine these
two architectures in order to recognize a large
vocabulary of Arabic words.
Ziadi, F., Ben Cheikh, I. and Jemni, M.
A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition.
DOI: 10.5220/0010814800003122
In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 213-220
ISBN: 978-989-758-549-4; ISSN: 2184-4313
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
213
The remainder of this paper is structured as
follows: Section 2 displays the main characteristics of
the Arabic script. Section 3 describes not only the
performance of convolutional network and LSTM,
but also various related works using this two models
in the recognition field.
Section 4 reviews the related works suggested in
the literature using respectively CNN model, LSTM
model, and the hybridation of these two models for
writing recognition. Section 5 introduces our
suggested approach. Then, section 6 reports our
preliminary results. Finally, section 7 summarizes the
paper and highlights major directions for future
research.
2 ARABIC LANGUAGE
CHARACTERISTICS
Thanks to its stability, Arabic is a rich language in
terms of its morphology, phonology and vocabulary
(Cheriet and Beldjehem, 2006) (Ben Hamadou, 1993)
(Kanoun,Alimi and Lecourtier, 2005) (Kammoun and
Ennaji, 2004). An Arabic word can be decomposed
into smaller units. If it derives from a root, it is said
to be decomposable into morphemes (root, prefix,
infix and suffix). Figure 2 illustrates our vision of
Arabic words derived from several roots and
conjugated according to different schemes.
Figure 2: Our vision of an Arabic word.
Based on this principle, we were able to enlarge our
vocabulary not only by combining words derived
from the same root with several schemes, but also by
applying conjugation rules (tense, number, gender,
etc.).
Figure 3 presents several samples of words that
are derived from the root and conjugated
according to different schemes. For instance, the root
derived according to the scheme gives the
verb word. The latter conjugated in the past
tense with the masculine plural pronoun “they” gives
the verb اوفرصنا.
Figure 3: Various words derived from the root “”.
In this context, we aim to classify Arabic word
roots using a convolutional network. The output of
this model will be used by a recurrent network
(LSTM) to recognize whole word.
3 CNN AND LSTM OVERVIEW
3.1 Convolutional Neural Network
Convolutional Neural Network (CNN), proposed by
LeCun (LeCun and Bengio, 1995), is a neural
network model that combines three key architectural
ideas, namely local receptive fields, shared weight
and spacial subsampling. This network receives 2-D
input (e.g. an image, a voice signal) and extracts high-
level characteristics through a series of hidden layers.
These layers consist of:
▪ Convolution layer includes a set of filters whose
parameters need to be learned. These filters have
the same shape as the input but with smaller
dimensions. In the learning process, the filter of
each convolutional layer is tiled across the input
space (in the case of an image, for example, the
filter is slid across the width and height of the
input) and the input product is calculated. This
calculation of the entire input leads to a filtering
feature map.
▪ Pooling layer operates upon each feature map. It
reduces the spatial size of the representation in
order to reduce the number of parameters and
computational time, and hence to also control
over-fitting. Max-Pooling is a common
approach that partitions the input space into
non-overlapping regions and chooses the
maximum value of each region.
▪ Fully connected layer takes the output of
convolution/pooling and predicts the best label
to describe the image.
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
214
3.2 LSTM Network
Recurrent neural networks (RNN) have recurrent
connections. At time t, the RNN takes into account a
number of past states, following the short term
memory principle. We can deduce that RNNs are
adapted for analyzing contextual applications, and
more particularly for processing sequences.
However, this “short-term memory” is not anymore
sufficient in most applications involving long time
differences (typically the classification of video
sequences). In fact, “classic” RNNs are only able to
memorize the so-called near past, and start to forget
after multiple iterations.
These networks take as input: 1) the current input
and 2) what they have observed previously over time.
The decision reached by a recurrent neuron at time t-
1 affects the learning process at instant t. However,
RNNs suffer from the vanishing gradient problem.
As more layers using activation functions are
added, the gradients of the loss function begin to
approach to zero. This problem hampers learning of
long data sequences and therefore makes the network
hard to train. LSTM networks provide a solution to
this problem.
In addition to its external connections, A LSTM
neuron has a recurrent-self connection with a constant
coefficient equal to 1. This allows us to save the
successive states of the neuron, varying, from one
iteration to another. Multiplication gates (input-gate,
output-gate and forget-gate) protect the current state
of memory (the cell state) and the memory of the
whole network. The structure of the LSTM neuron is
illustrated in Figure 4.
Figure 4: LSTM node architecture.
Equations (1) and (2) are used to calculate the
current memory state (Ct) and current output (ht),
respectively.
)
~
**(
1 ttttt
CiCfC +=
−
(1)
ttt
oCh *)tanh(=
(2)
Where C
t-1
is the previous memory cell, C
̃
is the
past value of the current memory. i
t
, o
t
and f
t
stand for
input-gate, output-gate and forget-gate, respectively.
These outputs are estimated by the current neuron
input and the previous neuron output.
4 RELATED WORKS
4.1 CNN for Writing Recognition
A special focus is put on Arabic word recognition.
Using CNN, various methods have been proposed
and high recognition rates have been reported for
handwriting recognition in English (Bai,Chen,Feng
and Xu, 2014) (Yuan, Bai, Jiao and Liu, 2012),
Chinese (Wu, Fan, He, Sun and Naoi, 2014) (Zhong,
Jin and Feng, 2015) (Yang, Jin, Xie and Feng, 2015)
(He, Zhang, Mao and Jin, 2015) (Zhong, Jin and Xie,
2015), Hangul (Kim and Xie, 2015), Malayalam
(Anil, Manjusha, Kumar and Soman, 2015),
Devanagari (Acharya, Pant, and Gyawali, 2015)
(Singh,Verma and Chaudhari, 2016), Telugu (Soman,
Nandigam and Chakravarthv, 2013).
The majority of these methods are based on
Convolutional Neural Networks. The first CNNs date
back to the 1980s with the work of K. Fukushima
(Fukushima, 1980), but it was in the 1990s that these
networks were popularized with the work of Y. Le
Cun et al. about character recognition (LeCun, Boser,
Denker, Henderson, Howard, Hubbardet and Jackel,
1990). In a study conducted by (Poisson and Gaudin,
2001), a particular neuron network TDNN (Time
Delay Neural Network) was implemented for
handwriting recognition. Their recognition results
show the input of a hidden Markov model.
4.2 LSTM for Writing Recognition
Unidirectional and bidirectional LSTM networks
have been tested in many handwriting applications.
In (Graves, Fernandez, Liwicki, Bunke and
Schmidhuber, 2008), a combined BLSTM-CTC
network was put forward in an online handwriting
dataset for unconstrained online handwriting
recognition. In a study carried out by (Graves and
Schmidhuber, 2009), a multidimensional LSTM was
combined with CTC for offline handwriting
recognition. (Su and Lu, 2015) set forth a
segmentation-free approach in order to recognize text
A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition
215
in scene images. A RNN was adapted to classify the
word sequential characteristics obtained using
Histograms of Oriented Gradients (HOG). (Graves,
Fernandez, Liwicki, Bunke and Schmidhuber, 2007)
offered a system that is able to manage online
handwritten data. It is based on a RNN with an output
layer designed for sequence labeling, associated with
a probabilistic language model. (Mioulet, 2015)
combined a bidirectional LSTM with a connectionist
temporal classification (BLSTM-CTC) for
handwriting recognition and keyword detection.
4.3 A Hybrid CNN-LSTM Network for
Arabic Recognition
Several works have combined CNN and LSTM
networks for Arabic language treatment.
(Hamdi et al., 2019) suggested an online Arabic
character recognition system based on hybrid Beta-
Elliptic model (BEM), CNN, bidirectional LSTM and
SVM. Online handwriting trajectory features are
extracted using BEM, while generic features are
extracted using CNN. The classification is achieved
by combining DBLSTM and SVM models.
To bridge the communication gap between deaf
and hearing communities, (Agrawal and Urolagin,
2020) introduced a 2-way Arabic sign language
translator using CNN-LSTM and NLP models for
translating texts into signs and vice versa. CNN layers
perform feature extraction and LSTM layers provide
temporal sign prediction.
(Al Omari, Al-Hajj, Sabra and Hammami, 2019)
recommended a combined CNN and LSTM model for
Arabic sentiment classification. This model was
tested on the Arabic Health Services (AHS) dataset.
It achieved more interesting results on the Main-
AHS and the Sub-AHS datasets compared with those
of the previous methods (Alayba, Palade, England
and Iqbal, 2018).
Since the combination of CNN and LSTM has
proven its effectiveness for improving the quality of
Arabic writing analysis, we use this hybridization of
different linguistic knowledge in order to recognize
Arabic decomposable words.
5 APPROACH PROPOSAL
With the recent emergence of deep learning
technology, the aforementioned research studies
described in previous sections inspired our work.
Thus, we have tried to combine two neural
network variants, namely CNN and LSTM models.
The first step of our work consists in
implementing a convolutional neural network that
learns and recognizes roots from word images. In the
second step, we feed a LSTM with word samples.
This LSTM is specialized in recognizing words
derived from the predicted root. It is conceived to
recognize the schemes and the flexional details of the
candidate word.
The CNN model comprises these different layers:
▪ A first convolutional layer with 64 filters,
each with the size of (3*3), followed by a
“relu” activation function.
▪ A max-pooling layer with a filter size (2*2).
▪ A second convolutional layer with 64 filters
each of size (3*3) and a “relu” activation
function.
▪ A max-pooling layer with a filter size (2*2).
▪ A flattening operation that flatten all data into
a single vector.
▪ A Dense layers where each neuron is
connected to all neurons in the previous layer.
“relu” is the activation function of this layer.
▪ A Dense layer with 10 neurons and a
“softmax” activation function to classify the
10 roots.
Note that the “Dropout” technique is used after
each layer to prevent overfitting.
Figure 5 shows the implemented CNN
architecture and decisions made at each layer.
Figure 5: CNN architecture.
As mentioned before, after the classification
phase, the recognized root and the whole word image
will be passed to the LSTM model. We have started
with a static segmentation where each word is split
into vertical bands of a constant number of columns
(25) as shown in Figure 6. Each band is converted into
a 150 sized vector (image width), where each value
corresponds to the sum of band row . At each time
step, LSTM will be fed by six vectors.
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
216
Figure 6: Splitting the word “ ”.
Figure 7: An example of the word “ ” recognition by
the LSTM network.
Figure 7 displays multiple information
propagation that allow the LSTM network to learn
word derivations and flexions with their
corresponding roots (CNN output).
In this example, at time t, the cell accepts two
information: 1) the root (It/he becomes
abundant) and 2) the first letter “ ” of the verb word
to be recognized “ ” (i.e. She has been
reproduced).
To recall these two relevant data, the forget gate
is activated (=1). Now, this neuron is going the try to
predict the conjugation time, the scheme, etc. and its
memory will save these information which will be
exploited by the next cell.
At t+1, the second letter (“ ”) is the input of the
LSTM cell, the last memory will be used to go on,
while the current memory will ignore the current
input (it is actually the first root consonant) and
decide to save conjugation information to propagate
to the following cell at t+2.
At t+2, the following cell will determine the time
(. i.e. past tense) and the scheme () thanks to
the stored information and relevant input “ ”. Details
concerning the second and third root letters at t+3 and
t+4 are not memorized.
Finally, at time t+5, the last letter of the word
(suffix) will determine the personal pronoun (. i.e.
She). Once the root, time, scheme and different
conjugation elements are known, the word is
reconstituted and well-recognized.
6 EXPERIMENTAL RESULTS
6.1 Dataset
Our approach is tested using a database that contains
Arabic printed word images, of various sizes and
fonts, derived from three consonant roots (see Figure
8), displays samples extracted from the APTI
database.
Figure 8: Samples of words extracted from the APTI
database.
The corpus includes a set of Arabic words images.
We manipulated 17600 samples of 1100 words
derived from tri-consonant roots following different
schemes. For data collection, we have gathered words
from the APTI database and organized them into 10
sets. Each set corresponds to one root and contains
many words that follow several schemes conjugation
A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition
217
forms (e.g. different tenses and pronouns). Each
word is given in 16 samples. The distribution of the
words samples is as follows : 60% for the training,
25% for the validation and 15% for the test.
6.2 CNN Results
Both CNN and LSTM architectures have been created
based on the Keras Framework. Before fitting the
CNN model, we must compile it. Several parameters
are necessary to carry out this phase like the
optimizer, the loss function and optionally the
evaluation metrics.
To compile the model,we choose the Root Mean
Square Propagation (RMSPROP) optimizer,
Categorical Cross-Entropy loss (also called Softmax
Loss) and accuracy metric to evaluate our model.
In order to adjust the parameters of a model, a set
of hyperparameters must be chosen appropriately. In
our work we have used hyperparameters that we have
adjusted manully. In fact, we choose them according
to our experience. We then train the model, evaluate
its accuracy and repeat the process. This loop is
repeated until a satisfactory accuracy is noted.
After the training phase, estimated at about ten
hours, the CNN model achieved a 97% classification
rate after 30 epochs.
Some false predictions were detected during the
classification phase, such as the images presented in
the following figure (Figure 9) where the predicted
label is the root “”, while the true root is “”
which is too similar to the predicted label.
Figure 9: Examples of word predictions derived from the
root.
Figure 10 and Figure 11 represent curves
illustrating the two metrics: accuracy and loss,
respectively.
Based on the two figures, we notice that:
▪ The classification rate is 97%.
▪ The plot of training loss decreases to a point
of stability.
▪ The plot of validation loss decreases to a point
of stability and has a small gap with the
training loss.
We can deduce that this is the case of a good fit.
Figure 10: Performance curves on training and validation
data.
Figure 11: Representative curves of loss function on
training and validation data.
6.3 LSTM Results
Before fitting it in the LSTM, each word is split into
vertical bands with a constant number of columns
(25). In the short term, we intend to operate the
vertical projection of a word and send bands
delimited by local minima.
As first, the LSTM model was evaluated using a
corpus composed of 5000 samples. The first results
are promising. In fact, the recognition rate of the
LSTM algorithm is equal to 87.2% .
In fact, false results are detected mainly because
of the segmentation phase. As mentioned, in this
work we started with a static segmentation where
each word sample is divided into 6 bands to detect
each letter of the word. Since we have words
composed of more than 6 letters as well as less than 6
letters, letters are not properly fed to the LSTM.
In addition, we will expand the lexicon by using
more than 100 roots. Hence, we build not only
vocabulary of about 4000 words, but also a corpus of
around 60000 samples for training and testing
a hybrid CNN-LSTM model.
We notice that: 1) the LSTM model browses the
entire word from right to left and learns to focus on
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
218
schemes and flections rather than on the consonants
of the root. 2) We are confident that our model is
reliable thanks to the ability of memory cells. The
latter are able to store information and help neighbour
neurons acquire relevant knowledge. Our model fits
well with the Arabic lexicon philosophy by taking
into account the significant coherence between
prefixes and suffixes and their complicity in
incarnating the time of an action verb and its
corresponding pronoun.
6.4 Comparison with Related Works
Table 1 illustrates models that combined CNN and
LSTM for Arabic recognition. It also indicates the
rates obtained on specific datasets.
Table 1: Arabic rates recognition.
Model
Dataset
Result
BEM + CNN +
BLSTM + SVM for
an online Arabic
character recognition
(Hamdi et al.,
2019)
LMCA
99.11 %
Online-Khatt
93.98 %
CNN-LSTM for
Arabic sign language
translator (Agrawal
and Urolagin,
2020)
Minisi MN.
Arabic sign
language
dictionary
88.67 %
CNN-LSTM for
textual reviews (Al
Omari, Al-Hajj,
Sabra and
Hammami, 2019)
Main-AHS
88 %
Sub-AHS
96 %
Ar-Twitter
84 %
ASTD
79 %
OCLAR
90 %
CNN-LSTM for
an Arabic sentiment
analysis (Alayba,
Palade, England
and Iqbal, 2018)
Main-AHS
94 %
Sub-AHS
95 %
Our CNN model
for classification
APTI Dataset
97 %
Our CNN-LSTM
for word recognition
APTI Dataset
87.2 %
7 CONCLUSIONS
In this work, we proposed a deep recurrent approach
based on a hybrid CNN-LSTM model for tri-
consonantal Arabic word recognition.
Preliminary experiments were carried out on a
corpus including 17600 samples collected from the
APTI Dataset. The CNN model achieved a 97%
classification rate in root word recognition.
Preliminary experiments with the LSTM model
gave a score equal to 87.2%. The obtained results are
motivating. The combination of LSTM and CNN is
expected to be more and more interesting, due to the
significant coherence between the LSTM model,
(with a special focus on its concepts of forgetting and
memory), and the Arabic philosophy of constructing
words around the root, playing with prefixes, infixes
and suffixes and respecting Arabic derivations and
conjugation patterns.
Our future works will put special focus on: 1) We
will expand the vocabulary to handle with hundreds
of roots for training, validating and testing the
performance of the network on large datasets. 2).
Apply cross-validation to demonstrate the reliability
of our model. 3) Experiment the reliability of the
LSTM model to achieve the recognition, notably, of
the scheme, the tense and the pronoun, while
changing the way of word segmentation.
REFERENCES
Ibrahim, M.N., Bilmas, S.N, Babiker, A. (2013). A
framework of an online self-based learning for teaching
arabic as second language (TASL).Fifth International
Conference on Computational Intelligence, Modelling
and Simulation, pp. 255-260.
Touj, S., Ben Amara, N., Amiri, H. (2007). A hybrid
approach for off-line arabic handwriting recognition
based on a planar hidden markov modeling. ICDAR’07,
Brazil, 964- 968J.
Avila, J.M. (1996). Optimisation de modèles markoviens
pour la reconnaissance de l'écrit. PHD Thesis,
University of Rouen.
Cheriet, M., Beldjehem, M. (2006). Visual processing of
arabic handwriting: challenges and new
directions.SACH’06, India, pp 1-21.
Ben Hamadou, A. (1993). Vérification et correction
automatiques par analyse affixale des textes écrits en
langage naturel. PHD Thesis, Faculty of Sciences of
Tunis.
Kanoun, S., Alimi, A., Lecourtier, Y. (2005). Affixal
approach for arabic decomposable vocabulary
recognition: a validation on printed word in only one
font. ICDAR’05, Seoul.
A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition
219
Kammoun, W., Ennaji, A. (2004). Reconnaissance de
textes arabes à vocabulaire ouvert. CIFED, France.
LeCun, Y., Bengio, Y. (1995). Convolutional networks for
images, speech, and time series. The handbook of brain
theory and neural networks, 3361(10), 1995.Bai, J.,
Chen, Z., Feng, B. ,Xu, B. (2014). Image character
recognition using deep convolutional neural network
learned from different languages.IEEE International
Conference on Image Processing (ICIP), pp. 2560-
2564.
Yuan, A., Bai, G., Jiao, L., Liu, Y. (2012). Offline
handwritten english character recognition based on
convolutional neural network. Document Analysis
Systems (DAS), 10th IAPR International Workshop, pp.
125-129.
Wu, C., Fan, W., He, Y., Sun, J., Naoi, S. (2014).
Handwritten character recognition by alternately
trained relaxation convolutional neural network.
Frontiers in Handwriting Recognition (ICFHR), 14th
International Conference, pp. 291-296.
Zhong, Z., Jin, L., Feng, Z. (2015). Multi-font printed
chinese character recognition using multipooling
convolutional neural network.Document Analysis and
Recognition (ICDAR), 13th International Conference,
pp. 96-100.
Yang, W., Jin, L., Xie, Z., Feng, Z. (2015). Improved deep
convolutional neural network for online handwritten
chinese character recognition using domain-specific
knowledge. Proc. Document Analysis and Recognition
(ICDAR), 13th International Conference, pp. 551-555.
He, M., Zhang, S., Mao, H., Jin, L. (2015). Recognition
confidence analysis of handwritten chinese character
with CNN.Proc. Document Analysis and Recognition
(ICDAR), 13th International Conference, pp. 61-65.
Zhong, Z., Jin, L., Xie, Z. (2015). High performance offline
handwritten Chinese character recognition using
GoogLeNet and directional feature maps.Proc.
Document Analysis and R (ICDAR), 13th International
Conference, pp. 846-850.
Kim, I.-J., Xie, X. (2015). Handwritten hangul recognition
using deep convolutional neural networks.
International Journal on Document Analysis and
Recognition (IJDAR), 18, pp. 1-3.
Anil, R., Manjusha, K., Kumar, S.S., Soman, K.P. (2015).
Convolutional neural networks for the recognition of
malayalam characters.Proc. Document Analysis and
Recognition (ICDAR), 13th International Conference,
pp. 1041-1045.
Acharya, S., Pant, A.K., Gyawali, P.K. (2015). Deep
learning based large scale handwritten devenegari
character recognition. 9th International Conference on
Software, Knowledge, Information Management and
Applications (SKIMA), pp. 1-6.
Singh, P., Verma, A., Chaudhari, N.S. (2016). Deep
convolutional neural network classifier for handwritten
devanagari character recognition. Information Systems
Design and Intelligent Applications: Proceedings of
Third International Conference INDIA, 2, pp. 551-561.
Soman, S.T., Nandigam, A., Chakravarthv, V.S. (2013). An
efficient multiclassifier system based on convolutional
neural network for offline handwritten Teugu character
recognition.Proc. Communications (NCC), 2013
National Conference, pp. 1-5.
Fukushima, K. (1980). Neocognitron: A self-organizing
neural network model for a mechanism of pattern
recognition unaffected by shift in position. Biological
cybernetics, 36(4):193–202.
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard,
R.E., Hubbardet, W., Jackel, L.D. (1990). Handwritten
digit recognition with a backpropagation network.
Advances in Neural Information Processing Systems,
pages 396–404.
Poisson, E., Viard-Gaudin, C. (2001). Réseaux de neurones
à convolution: reconnaissance de l’écriture manuscrite
non contrainte.Polytechnic School of the University of
Nantes, France.
Graves, A., Fernandez, S., Liwicki, M., Bunke, H.,
Schmidhuber, J. (2008). Unconstrained online
handwriting recognition with recurrent neural
networks.Advances in Neural Information Processing
Systems, 20:1-8.
Graves, A., Schmidhuber, J. (2009). Offline handwriting
recognition with multidimensional recurrent neural
networks.Advances in Neural Information Processing
Systems, 21:545-552.
Su, B., Lu, S. (2015). Accurate scene text recognition based
on recurrent neural network. In: Cremers D., Reid I.,
Saito H., Yang MH. (eds) Computer Vision -- ACCV
2014. ACCV 2014. Lecture Notes in Computer Science,
vol 9003. Springer, Cham.
Graves, A., Fernandez, S., Liwicki, M., Bunke, H.,
Schmidhuber, J. (2007). Unconstrained online
handwriting recongnition with recurrent neural
networks.Advances in Neural Information Processing
Systems.
Mioulet, L. (2015). Reconnaissance de l’écriture
manuscrite avec des réseaux récurrents, PhD Thesis,
University of Rouen.
Hamdi, Y., et al. (2019). Hybrid DBLSTM-SVM based
Beta-elliptic-CNN Models for Online Arabic
Characters Recognition. 2019 InternationalConference
on Document Analysis and Recognition (ICDAR).
IEEE.
Agrawal, T., Urolagin, S. (2020). 2-way Arabic Sign
Language translator using CNNLSTM architecture and
NLP.Accepted in ACM, Ei Compendex and Scopus, ISI
Web of Science, International Conference on Big Data
Engineering and Technology BDET-20.
Al Omari, M., Al-Hajj, M., Sabra, A., Hammami, N.
(2019).Hybrid CNNs-LSTM Deep Analyzer for Arabic
Opinion Mining.2019 Sixth International Conference
on Social Networks Analysis, Management and Security
(SNAMS).
Alayba, AM., Palade, V., England, M., Iqbal, R. (2018). A
combined CNN and LSTM model for arabic sentiment
analysis.In International Cross-Domain Conference for
machine learning and knowledge Extraction, pp.197-
191.
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
220
