=Paper=
{{Paper
|id=Vol-1988/LPKM2017_paper_5
|storemode=property
|title=Optical Character Recognition For Arabic Language Using Neural Network
|pdfUrl=https://ceur-ws.org/Vol-1988/LPKM2017_paper_5.pdf
|volume=Vol-1988
|authors=Abdelkarim Mars,Georges Antoniadis
|dblpUrl=https://dblp.org/rec/conf/lpkm/MarsA17
}}
==Optical Character Recognition For Arabic Language Using Neural Network==
https://ceur-ws.org/Vol-1988/LPKM2017_paper_5.pdf
Optical Character Recognition For Arabic language
using neural network
Abdelkarim Mars
Sience&Technique loboratory
Time Higher School
abdelkarim.mars@gmail.com
Abstract. As part of the DocumentToText project, which is being piloted
by TIME University and Horizon data society, we are working to develop
an Optical Character Recognition engine to convert all scanned books that
exist in Tunisian University into editable textual documents . In this article
we present our approach for the development of an OCR system as well as
the presentation of the utility of using the artificial neural networks for
Arabic characters. We will present the realization context, our point of
view on the particularity of the Arabic language with regard to the litera-
ture and finally the reasons that have governed the decisions taken in the
steps of the realization.
Keywords: OCR; Artificial neural network; Arabic character.
1 Introduction
The purpose of the recognition of writing is to transform a written text into a ma-
chine-readable representation easily reproducible by word processor. This task is not
easy because the words have an infinity of representations because each person have
his own writing, and because each writing can be represented by many fonts and
many styles (bold, italic, underline, shaded) and each writing have a different layouts.
Depending on the type of writing that a system must recognize (manuscript, cursive
or printed), the operations to be carried out and the results vary significantly.
The Optical Character Recognition technology (OCR) knows several practical ap-
plications in several fields of activity Among which we can cite:
• Banks and insurance for the authentication of bank checks (Correspondence be-
tween amounts and wording on the one hand and correspondence between the
identity of the signatory and its signature, on the other), and the verification of
clauses contracts for insurance.
• Mail for address reading and automatic mail sorting.
• Police and security for the recognition of mineralogical numbers for the control,
authentication and identification of manuscripts and identification of the writer.
The Arabic character recognition is a large problem [1]. This problem is due to the
characteristic of the Arabic language[2]. In this project we will work with Modern
Standard Arabic (MSA) wich is a standardized version used for official communica-
tion across the arab world [3].
Earlier surveys presented both printed character and handwriting, with more dis-
cussion about machine-print [4][5]
Our project uses a neural networks approach to recognize the Arabic characters. We
will use the Multi Layer Perceptron to learn our OCR system [6]. MLP use backpro-
pagation network to minimize the errors[7] in trainer model. It is simply a gradient
descent method to minimize the cost of the total squared error of the result computed
by the MLP network.
2 Characteristics of the Arabic language
Arabic is the 8th most spoken language in the world, with more than 400 million
speakers [8]. Yet, with respect to character recognition technologies, even those of the
leaders in dematerialization, performances are low of those for Latin characters.
The most important step on the OCR engines rely on graphical analysis of the im-
age to identify shapes and characters and use their reconciliation method to reconcile
characters.
Arabic writing, on the other hand, has its own characteristics which pose difficul-
ties for the engines: [9]
• Arabic is a Semitic language: it uses three-letter roots where vowels are not always
written. The engine has difficulties to reconstruct the words.
• The diacritical signs (compulsory signs or which facilitate reading) accompany
each word. Thus, during the preprocessing step, these signs can be suppressed by
the automatic image enhancement functions (which is particularly preferred for old
and / or damaged documents) and thus alter the expected result.
• Graphically, the shape of the characters is lying on the line and not vertical like
most other writings. Moreover, it is a cursive writing and the continuity of the
characters weakens the segmentation necessary for the identification of characters.
• Arabic letters change their shapes depending on their position in the word;
isolated, initial, middle, end (Table 1).
Table 1. Change of the shape of a letter according to its position, example of
variation of the letter “ عAyn”.
There are, therefore, a number of obstacles inherent in the specificities of the lan-
guage which may alter the recognition of Arabic characters. However, technologies
have evolved and are still evolving to bring more performance and improve the quali-
ty of results.
3 Pretreatment steps
Because of the high granularity of the sampling and the various problems lighting
and seizure, the image of the character may suffer defects. These problems should be
corrected, if possible, before any analysis. Moreover, it is not always useful to use all
the points of the image Character to extract the characteristic properties. A reduction
step eliminates redundant points. The pretreatment techniques are as follows:
3.1 Smoothing
The image of the character may be tainted by noise due to artifacts acquisition and
often to the quality of the document, leading either to absences from points (holes)
either to impasto or excrescences and therefore to an overload of points. Smoothing
techniques solve these problems.
3.2 Standardization of size
The size of a character can vary from one writing to another, which can cause an in-
stability of the parameters. A natural pretreatment technique consists in bring the
characters to the same size. The normalization algorithm we used is imported from
OpenCV1 python library.
3.3 Thinning
The goal of slimming a character is to simplify the image of the character into an
easier image to be treated, for example by reducing it to one dimension, that is, the
thickness of the character is reduced to one pixel.
4 Neural network approach
There are many methods to train and model an OCR system. Among the existing
methods, we mention the neural networks, the neighbor k-nearest, hidden Markov
model (HMM), expert systems.
1
http://docs.opencv.org/2.4/modules/refman.html
On our OCR engine we used a technology based on the neural networks that have
been present in the Machine Learning community for decades, winning each year in
maturity and answering ever more challenges.
Learning an array of artificial neurons involves the following steps:
• Acquisition of data forming the learning base.
• Pre-processing: it consists of locating, segmenting and normalizing representations.
• Choice of attributes: after the pre-processing, we must extract attributes that define
the data. These attributes serve as network entries of neurons.
Before the processing of the data, we have to make the choice of the objects, the defi-
nition of the attributes characterizing the objects and the construction of the base
learning. At the end of this phase we obtain a table of numbers at two inputs: data and
attributes that characterize them.
Learning consists of presenting the examples sequentially and modifying the syn-
aptic weights according to an equation called the learning equation.
Artificial Neural Network consist of simple processing elements and a very high
degree of interconnection [10]. The weights of the network are learned from training
data. The weight are intialized into the initialized input layer, hidden layers and on the
final output layer. We have user the cross entropy function to compute the error rate.
The extracted information from data will be processed from input layer to output layer
gives a character in this task.
We have developed this algorithm for learning our artificial neural network
[11]:
Definition and allocation of the ANN
Initialization of all weights
Construction of a standardized training base
For each example e from learning base
Resampling of Example e
Standardization of example e
Extraction and saving all the features of the example e
Saving the label of example e
While the stop condition is not satisfied
For each example e from to the learning base
Propagation of example e
Calculate the local error
Checking the stop condition
End while
// Calculate the criterion
For each example e from the learning base
Propagation of example e
Calculate the local error
Calculating the cumulative error
To calculate the errors we use the following function:
ECE = -Σ [dj log(yj) + (1 - dj) log(1 - yj)]
5 Evaluation of the OCR System
In the acquisition step, a database should be acquired representing the different Arabic
characters. For this reason, we have used a book already scanned from the library (Le
cahier de la Tunisie) for the segmentation of words into characters. We were able to
extract all the characters that compose the book using the OpenCV library. At the end
of this operation, we were able to obtain 100,000 clean characters. The algorithm used
in this step uses the Python language.
Once the data are ready we have applied the following pretreatments:
• Cleaning and thinning the image of each character in the database.
• Normalization of the size of a character.
• Centering the image.
• Extraction of attributes.
The classification is done by a network of multilayer perceptron neurons, using the
Back propagation algorithm [11].
In order to test our system we used 80% of data for the training of our system and
20% for the test.
Once we finished learning our model, we passed the test data to our system and we
got an accuracy of 92%. The results obtained are encouraging, but still require some
improvements to begin the conversion of scanned books into texts.
6 Conclusion
We proposed in this article an Optical character recognition system for Arabic lan-
guage based on neural networks approach. We suggest a method bases on Multi Layer
Perceptron classifier, which allow an effective results and a high accuracy. In addi-
tion, a neural networks approach allows us to reduce the computational complexity by
exploiting the redundancy of the scanning letter. Otherwise, our OCR system still
require some improvements. We need to increase the size of our dataset and maybe
using deep learning approach to train a new model.
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