optical character recognition using artificial neural network - ijater

International Journal of Advanced Technology & Engineering Research (IJATER) 

OPTICAL CHARACTER RECOGNITION USING 

ARTIFICIAL NEURAL NETWORK 

Sameeksha Barve, computer science department, Jawaharlal institute of technology borawan, khargone (m.p) 

Email- Sameekshabarve69@gmail.com, vinibarve1889@gmail.com 

Abstract 

Optical character recognition refers to the process of translat 

ing images of hand-written, typewritten, or printed text into a 

format understood by machines for the purpose of editing, 

indexing/searching, and a reduction in storage size. Optical 

character recognition is the mechanical or electronic translation 

of images of handwritten, typewritten or printed text 

into machine-editable text. Artificial neural networks are 

commonly used to perform character recognition due to their 

high noise tolerance. In this paper, an Optical character recognition 

based on Artificial Neural Networks (ANNs). The 

ANN is trained using the Back Propagation algorithm. 

Introduction 

Optical Character Recognition, or OCR, is the process of 

translating images of handwritten, typewritten, or printed 

text into a format understood by machines for the purpose of 

editing, indexing/searching, and a reduction in storage size. 

Optical Character Recognition that would use an Artificial 

Neural Network as the backend to solve the classification 

problem. OCR is a field of research in pattern recognition, 

artificial intelligence and machine vision. Though academic 

research in the field continues, the focus on OCR has shifted 

to implementation of proven techniques. The input for the 

OCR problem is pages of scanned text. To perform the character 

recognition, our application has to go through three 

important steps. The first is segmentation, i.e., given a binary 

input image, to identify the individual glyphs (basic 

units representing one or more characters, usually contiguous). 

The second step is feature extraction, i.e., to compute 

from each glyph a vector of numbers that will serve as 

input features for an ANN. This step is the most difficult in 

the sense that there is no obvious way to obtain these features. 

The final task is classification. In our approach, there 

are two parts to this. The first is the training phase, where we 

manually identify the correct class of several glyphs. One of 

the most classical applications of the Artificial Neural Network 

is the Character Recognition System. This system is 

the base for many different types of applications in various 

fields, many of which we use in our daily lives. Cost effective 

and less time consuming, businesses, post offices, 

banks, security systems, and even the field of robotics employ 

this system as the base of their operations. 

Steps of OCR 

Optical Character Recognition (OCR) Using Artificial Neural 

Network is basically in the field of research. To gain better 

knowledge, techniques and solutions regarding the procedures 

that we want to follow, we studied the various research 

papers on existing OCR systems. All these study 

helped us with clarifying our target goals. The basic steps 

involved in Optical Character Recognition are:- 

1. Image Acquisition 

2. Preprocessing 

3. Document Page Analysis 

4. Feature Extraction 

5. Training and Recognition 

6. Post Processing 

Figure 1. Steps of OCR 

Different Areas of Character Recognition 

Optical Character Recognition deals with the problem of 

recognizing optically processed characters. Optical recognition 

is performed off-line after the writing or printing has 

been completed, as opposed to on-line recognition where the 

computer recognizes the characters as they are drawn. Both 

hand printed and printed characters may be recognized, but 

the performance is directly dependent upon the quality of the 

input documents. 

ISSN NO: 2250-3536 VOLUME 2, ISSUE 2, MAY 2012 139


The second branch of reading machines is that of page 

readers for text entry, mainly used in office automation. 

Here the restrictions character set is exchanged for constraints 

concerning font and printing quality. The reading 

machines are used to enter large amounts of text, often 

in a word processing environment. These page readers 

are in strong competition with direct key-input and 

electronic exchange of data. This area of application is 

therefore of diminishing importance. 

3. Process Automation: - 

This is actually the technology of automatic address 

reading for mail sorting. Hence, the goal is to direct 

each letter into the appropriate bin regardless of whether 

each character was correctly recognized or not. The 

general approach is to read all the information available 

and use the postcode as a redundancy check. 

Figure 2. Areas of Character Recognition 

The more constrained the input is, the better will the performance 

of the OCR system be. However, when it comes to 

totally unconstrained handwriting, OCR machines are still a 

long way from reading as well as humans. However, the 

computer reads fast and technical advances are continually 

bringing the technology closer to its ideal. 

Applications of OCR 

Algorithm Used For Optical Character 

Recognition 

One of the most typical problems to which a neural network 

is applied is that of optical character recognition. Recognizing 

characters is a problem that at first seems extremely 

simple- but it's extremely difficult in practice to program a 

computer to do it. And yet, automated character recognition 

is of vital importance in many industries such as banking 

and shipping. The U.S. post office uses an automatic scanning 

system to recognize the digits in ZIP codes. We may 

have used scanning software that can take an image of a 

printed page and generate an ASCII document from it. These 

devices work by simulating a type of neural network known 

as a back propagation network. 

Three main application areas are commonly distinguished; 

data entry, text entry and process automation 

1. Data Entry:- 

This area covers technologies for entering large 

amounts of restricted data. Initially such document reading 

machines were used for banking applications. The 

systems are characterized by reading only an extremely 

limited set of printed characters, usually numerals and a 

few special symbols. They are designed to read data like 

account numbers, customer’s identification, article 

numbers, amounts of money etc. 

2. Text Entry:- 

Figure 3. Back propagation Network Architecture 

A Back Propagation network learns by example. We give 

the algorithm examples of what we want the network to do 

and it changes the network’s weights so that, when training 

is finished, it will give the required output for a particular 

input. Back Propagation networks are ideal for simple Pattern 

Recognition and Mapping Tasks As just mentioned, to 



train the network we need to give it examples of what we 

want the output (called the Target ) for a particular input as 

shown in Figure 3. 

. 

An Artificial Neural Network (ANN), usually called neural 

network (NN), is a mathematical model or computational 

model that is inspired by the structure and/or functional aspects 

of biological neural networks. A neural network consists 

of an interconnected group of artificial neurons, and it 

processes information using a connectionist approach 

to computation. In most cases an ANN is an adaptive system 

that changes its structure based on external or internal 

information that flows through the network during the learning 

phase. Modern neural networks are nonlinear 

statistical data modeling tools. They are usually used 

to model complex relationships between inputs and outputs 

or to find patterns in data. An artificial neural network 

(ANN), usually called “neural network” (NN), is a mathematical 

model or computational model that tries to simulate 

the structure and/or functional aspects of biological neural 

networks. It consists of an interconnected group of artificial 

neurons and processes information using a connectionist 

approach to computation. In most cases an ANN is an adaptive 

system that changes its structure based on external or 

internal information that flows through the network during 

the learning phase. 

Figure 4. Back propagation Training Set 

So, if we put in the first pattern to the network, we would 

like the output to be 0 1 as shown in figure 4 (a black pixel is 

represented by 1 and a white by 0 .The input and its corresponding 

target are called a Training Pair. 

Figure 6. Artificial neural network 

Figure 5. Applying a training pair to a network 

Many of today's document scanners for the PC come with 

software that performs a task known as optical character 

recognition (OCR). OCR software allows we to scan in a 

printed document and then convert the scanned image into to 

an electronic text format such as a Word document, enabling 

you to manipulate the text. In order to perform this conversion 

the software must analyze each group of pixels (0's and 

1's) that form a letter and produce a value that corresponds to 

that letter. Some of the OCR software on the market use a 

neural network as the classification engine. 

Artificial Neural Network 



Figure 7 .Example of OCR 

Conclusion 

At the current stage of development, the software does perform 

well either in terms of speed or accuracy but not better. 

It is unlikely to replace existing OCR methods, especially 

for English text. Artificial neural networks are commonly 

used to perform character recognition due to their high noise 

tolerance. The systems have the ability to yield excellent 

results. The feature extraction step of optical character recognition 

is the most important. A poorly chosen set of features 

will yield poor classification rates by any neural network. 

References 

[1] Optical Character Recognition using Artificial Neural 

Networks Rakesh Bhujade, BLB-International Journal of 

Science & Technology Vol.1, No. 2 (2010), 143-152 (ISSN 

0976-3074) 

[2] Character Recognition Using Neural Networks by Near 

East University, North Cyprus, Turkey via Mersin-10, 

KKTC 

[3] Some Considerations on the Limitations of Image 

Processing Computer Architectures by Michael J. B. Duff, 

Department of Physics and Astronomy University College 

London Gower Street, London WClE 6BT 

[4] Optical character recognition by The Association for 

Automatic Identification and Data Capture Technologies 

[5] Online Handwritten Character Recognition Using 

an Optical Back propagation Neural Network by Walid A. 

Salameh Princess Summaya University for Science and 

Technology, Amman, Jordan

optical character recognition using artificial neural network - ijater

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