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Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 307
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IJCSMC, Vol. 4, Issue. 1, January 2015, pg.307 – 313
RESEARCH ARTICLE
OCR-ANN Back-Propagation Based Classifier
Asmaa Qasim Shareef
1 Sukaina M. Altayar
2
¹,2
College of Science, University of Baghdad, Iraq 1 [email protected] 2 [email protected]
Abstract— Optical Character Recognition by using Neural Network is a prototype system that is useful to
recognize the character. pre-processing for document is the preliminary step for the recognition to be
accurate, which transforms the data into a format that will be processed easily and effectively. The main
task of pre-processing is to decrease the variation that causes a reduction in the recognition rate and
increases the complexities. In this paper, many pre-processing techniques has been used to improved
OCR accuracy by pre-processing the original image and its character spiritedly, which includes noise
filtering, smoothing, thresholding, and skewing. The experimental results show that the improvement of
recognition side has achieved a good result for many types of noisy and non-uniform characters
document. The efficiency and recognition testes for training method has been performed and reported in
this paper. This paper shows how the use of artificial neural network for an optical character
recognition application, while achieving highest quality of recognition and good performance by
applying multiple image processing technique.
Keywords— Optical Characters Recognition; Back-Propagation; Neural Network; Image Analyses
I. INTRODUCTION
The Optical Character Recognition (OCR) is the process of automatic recognition for different
characters from an image documented, and also provides a full alphanumeric recognition of printed or
handwritten characters, text, numerals, letters and symbols into a computer process able to be formatted.
OCR has gained largely impetus due to its application in the fields of Computer Vision, Intelligent Text
Recognition applications and Text based decision-making systems [1,2]. The approach is taken as an
attempted to solve the OCR problem, is based on psychology of the characters as perceived by the humans.
Thus, the geometrical features of a character and its variants will be considered for recognition.
The addition of the neural networks allow improvement on the recognition of arbitrary font styles
as opposed to a standard font. The difference in font and its sizes of a scanned image make recognition
process difficult if no pre-processing applied. In most document images that are gotten from scanning,
allow a noisy pixels to be found; In addition, width of the stroke is also a factor that affects recognition,
therefore; to get a good character recognition accuracy an elimination to the noise after reading binary
Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 308
image data is needed, in addition to smooth image for better recognition, and extract features efficiently,
train the system and classify patterns [3,4].
II. RELATED WORK
[6] proved that the Succession in OCR depends on two factors, which are feature extraction and
classification algorithms.[7] applied neural network approach to perform high accuracy recognition on
music score with backward propagation. [8] presented scheme to develop a complete OCR system for
different five fonts and sizes of characters, and implemented the steps of the OCR system: pre-processing,
feature extraction, segmentation, and classification. The artificial neural network (ANN) has been used for
classification purpose. [9] explained the classification methods based on learning from examples and its
application to character recognition.
III. PROPOSED SYSTEM ARCHITECTURE
This paper presents a procedure for designing OCR-ANN system, which recognized text from a
scanned image. The model built using C# language with Open CV library. The system consists of two parts
shown in Fig.1: Training ANN Part and Pattern Recognition part.
Fig.1 OCR-ANN system scheme
In 1st part, the database of characters has been created based on training ANN on images of
characters with Backpropagation (BP) to generate data matrix that was used for classification operation. In
2nd
part multiple processing have been used to recognize charterers from scanned image includes; pre-
processing and classification.
ANN Training Part
This part used to generate data that used in recognition part. It could be represented by the flow chart
in Fig.2. The BP algorithm has been applied to feed-forward multilayer neural network (FFML). The nodes
are organized in layers, and send their signals forward with errors propagated backwards. The network
receives inputs by neurons in the input layer, and the output of the network is obtained by the neurons on an
output layer with one hidden layers. Each layer is fully connected to the next layer. The learning
continue until the error is reduced, i.e. the ANN learns the training data. The training starts with random
weights, and aim is to adjust them to arrive at minimal error. The number of layers and the number of
neurons per layer are important decisions to make when applying this architecture. The complexity between
Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 309
the input data and desired output determines the number of nodes in the hidden layer. Also, the amount of
training data sets set an upper bound for the number of nodes in the hidden layer. This upper bound is
calculated by dividing the number of input output pairs examples in the training set by the total
number of input and output nodes in the network. Then divide the result by scaling factor between five and
ten [10].
Pattern Recognition Part
This part of scheme will imported the scanned image through pre-processing operations to recognition
operation to be compared with the trained images in order to classify each character. pre-processed
operation is to enhance and segment the characters to prepare them for recognition process. Image
binarization (thresholding) is used for edge/boundary detection [11].
Fig.2 Input/output Data matrix Generation scheme
IV. EXPERIMENTAL RESULTS
The first step of programming is to generate DataMatrix file that be used later in the recognition
process. The program will request font from operation system, then it will convert each character into an
image, it will be used as an input data to BP-NN. There is an option to add some noise to characters' image,
in order to enhance the recognized result by making ANN trained with non-uniform characters, this causes
an increasing in the time of training. The second part is to initialized FFML with BP to adjust the weights.
After training was completed, the weights were saved in DataMatrix file. Fig.3 shows the BP-ANN front
page.
Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 310
Fig.3 BP-ANN Training Dialog.
After completing the training part, recognition part then started. First document image either was
loaded from image files or through an optical device, and load training weights to be used in recognition.
Fig.4 shows two samples of images has been taken to compare results: white background and graduate
lighting images.
(a) (b)
(b) (d)
Fig.4 Binarazation of document images for
(a) Character image with white background (b) the binaraized image (c) Character image with graduate lights background
(d) the binaraized image
Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 311
From Fig.5 it shows the Comparison between two different background documents the first image that
is character image with white background and second are character image with graduate lights background.
As appeared there are no noisy background from first image Fig.6-b because of no graduate lighting which
makes recognized process with good accuracy, the problem appears with colour and graduate lights
background images it makes classification process difficult due to noisy background as in Fig.6-d. The
recognition result for both is shown in Fig.7
(a) (b)
Fig.5 Recognition of characters for
(a) Character image with white background and (b) Character image with graduate lights background
Fig.6 Applying pre-processing algorithms on graduate lights background
A sample of character images with white background has been used in test. As shown in Fig.7 the
background is graduate in lighting duo to scanning process, also it has mirrored characters, this makes more
noise and unwanted features that makes recognition process has many errors.
(a) (b)
Fig.7 Scanned Character image
(a) Original scanned Image (b) binarazation of image
Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 312
When used ANN to recognized the image, it obviously that recognition will field to identify the
actual characters and it will have many errors as shown in Fig.8. After used pre-processing enhancement
(smoothing, adaptive threshold and noise filter), the recognition process has been removed noisy
background and recognized just characters with good accuracy.
Table-1 shows the results of recognition for all the previous tested images that either use pre-processing or
without using it.
(a) (b)
Fig.8 Recognition results
(a) Recognition by ANN without used pre-processing
(b) Recognition by ANN with used pre-processing
Table 1:Recognition Results for trained document images
Type of Images Number of
characters
Recognition Result
No Pre-processing With Pre-processing
Detect Failed Eff. % Detect Failed Eff. %
1 Computerized Clear
White background 36 35 1 97.22 36 0 100
2
Computerized
Graduate lights
background
71 64 7 90.14 71 0 100
3 Scanned image with
complex background 396 334 773 43.20 334 62 84.34
v. Conclusion Using BP-ANN in OCR gives good results. However, the OCR affects by many factors like noise,
brightness, coloured background. So pre-processing is necessary to be used in documented images as an
initial step for character recognition systems to remove the effects of these factors. Each image requires
different pre-processing techniques depending on the effect of the factor that may affect the quality of it.
ACKNOWLEDGEMENTS
Our thanks to Referee who read our paper and to the experts who contributed towards development of the
template, also our thanks to the Editorial Support Team, International Journal of Computer Science and
Mobile Computing.
Asmaa Qasim Shareef et al, International Journal of Comp. Science and Mobile Computing, Vol.4 Iss.1, January- 2015, pg. 307-313
© 2015, IJCSMC All Rights Reserved 313
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