ocr-ann back-propagation based classifierasmaa qasim shareef1 sukaina m. altayar2 ¹,2college of...

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



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



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.



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



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



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.



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