A SEMINAR REPORT
ON
TAMIL CHARACTER RECOGNITION
USING NEURAL NETWORKS
SPONSORED BY: MODULAR INFOTECH
SUBMITTED BY
AKSHAY APTE
HARSHAD GADO
SOURABH BARVE
Project guide:
RAM PATTARKINE
Professor and H.O.D (E&TC)
DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATION ENGINEERING
PUNE VIDYARTHI GRIHA’S
COLLEGE OF ENGINEERING AND TECHNOLOGYPARVATI,PUNE-411030.
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Certificate
This is to certify that
Akshay Apte
Harshad Gado
Sourabh Barve
of B.E (Electronics &Telecommunication Engineering.) has delivered a seminar entitled
“TAMIL CHARACTER RECOGNITION”
during the academic session 2007-08 in satisfactory manner in the partial fulfillment of the
requirement for the degree of “Bachelor of Engineering” in Electronics & Telecommunication
Engineering of Pune University , Pune under my guidance and supervision
Internal guide
PROFESSOR K.J.Kulkarni
DEPT. OF ELECTRONICS & TELECOMMUNICATION ENGINEERING
Pune Vidyarthi Griha’s
COLLEGE OF ENGINEERING & TECHNOLOGY, PUNE. 2007-08
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ACKNOWLEDGEMENTS
We would like to thank MODULAR INFOTECH for giving us an
opportunity to work on this project. We are grateful to them for their timely
assistance and co-operation.
We would also like to thank our project guide Professor Ram Pattarkine
for his able guidance. We acknowledge your valuable criticism as well as
timely appreciation.
Finally we would like to express our gratitude towards Professor
K.J.Kulkarni who went out of her way to help us.
HARSHAD GADO
AKSHAY APTE
SOURABH BARVE
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INDEX
•
ABSTRACT………………………….…………………………………………………..…………………..……….5
•
SPECIFICATIONS……………………………………………………….………………………………..………..6
•
FLOWCHART……………..…………………………………………………………….………….…….………..7
-
Training phase
-
Testing phase
•
Features of MATLAB…………………………………………………….………………………….…….…….10
•
Block diagram..…………………………………………………………….….….....11
•
What is NEURAL NETWORK?..............................................................................12
•
Working .…………….…………………………………………………….….……..13
•
Features extracted ..………………………………………………….…….……… 14
•
Models of neural networks under consideration…………………………..………19
-
Bi-directional Associative Memory……………………………………….……19
-
Neocognitron…………………………………………………………….………20
-
Radial Basis Function Network……………………………………….………..21
•
Applications …………………………………………………………..….….……....22
•
Current status…………………………………………………………….....……....23
•
Future plan………………………………………………………..…….…..…….....24
•
Bibliography…………………………………………………………………..……..25
•
Set of TAMIL characters………..…………………………………...……..………26
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FIGURE NUMBER
TITLE OF THE FIGURE
PAGE NUMBER
1
Block diagram
11
2
Character height
14
3
Characters having closed loops
15
4
Number of Horizontal Transitions
16
5
Maximum number of vertical transitions
17
6
Number of pixels in different quadrants
18
7
Bi-directional associative memory
19
8
Neocognitron model
20
9
Radial Basis Function Network
21
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ABSTRACT
Many of today’s document scanners for the PC come with the software that performs a task of
character recognition. OCR software allows you to scan a printed document and then convert
the electronic text in word format.
In our project we try to recognize characters of an Indian regional language. No such software is
presently available in the market which is satisfactorily providing the required results. We scan
a Tamil document and then recognize the characters using Neural Networks by extracting
their features.
Today India is considered to be a huge market for all kinds of products. But many parts of the
country are still not that well conversed with the English language. So to communicate with the
people at grass root level or even the residents of that particular region, local or regional
languages have to be used for communication. Thus to have the documentations in regional
languages is the need of today. So after thorough discussions and analyzing the needs of the
market we decided to make this project.
Though a very small part of digital image processing we find that the implementations of OCR in
the corporate world are huge. It can be used in banking & other financial institutions, libraries,
convert existing books into computer format so the books can be taken on a CD-ROM or
directly uploaded on the internet.
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SPECIFICATIONS

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
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Language : Tamil
Software used : MATLAB
Character size : 5mm-15mm
Expected accuracy : 80%
Color of page on which data is present : White
Color of the character : Black
Number of people from whom samples will be taken : 50
- Number of sets of samples to be taken from each person: 10
- Each set will consist of every character of the concerned regional
language.
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TRAINING PHASE
FLOWCHART
Start
Scan the handwritten document
Take one character at a time
Extract features of the character.
Give the feature vector to the
neural network
Update weights of the neural
network.
END
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TESTING PHASE
FLOWCHART
Start
Scan the handwritten document
Take one character at a time
Extract features of the character.
Give the feature vector to the
neural network
Print the character
END
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FEATURES OF MATLAB
MATLAB is a highly productive development environment for creating scientific and
engineering algorithms and applications, providing powerful tools for every step in the process.
The high level language, interactive programming environment, built-in Math functions, editing
and debugging tools, and deployment options all contribute to your overall development time.
With MATLAB you don’t have to develop algorithms from scratch or work with
complicated interfaces to external libraries as is common with C and C++. You can write
IMAGE PROCESSING algorithms in MATLAB just as you would express them
mathematically. Most algorithms in the Image Processing toolbox are implemented in open
MATLAB language, which allows you to learn from and customize existing algorithms.
Building applications around complex algorithms and graphics is easy with interactive
GUIDE (Graphical User Interface Development Environment) tool, which lets you design and
modify user interfaces with list boxes, pull-down menus, push buttons, radio buttons, and sliders
as well as MATLAB plots and ActiveX controls. Alternatively, you can create GUIs
programmatically using MATLAB functions.
You have several options for deploying your application. MATLAB lets you
automatically convert your MATLAB programs into self-contained applications, and software
components to share with end users. Applications and components created using the compiler do
not require MATLAB to run. MATLAB builder for COM enables you to easily convert
MATLAB algorithms to Common Object Model (COM) objects that are accessible from any
com-based application.
MATLAB supports standard data and image formats, including JPEG, TIFF, PNG, HDF,
HDF-EOS, FITS, XLS, ASCII and BINARY files. It also supports multiband image formats,
such as LANDSAT. Low level input/output functions enable you to develop custom routines for
working with any data format.
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BLOCK DIAGRAM
Fig 1 : BLOCK DIAGRAM
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What IS Neural Network?
Neural networks are simplified models of biological nervous system. It is highly interconnected
network of a large number of processing elements called neurons in an architecture inspired by
the brain. A neural network can be massively parallel and therefore is said to exhibit parallel
distributed processing.
Neural network exhibit characteristics such as mapping capabilities or pattern association,
generalization, robustness, fault tolerance and parallel and high speed processing. Neural
network learn by examples. They can therefore be trained with known examples of a problem to
acquire knowledge about it.
Neural network adopts various learning mechanisms of which supervised learning and
unsupervised learning methods have turned out to be very popular.
In supervised learning, a ‘teacher’ is assumed to be present during the learning process. That is
the network aims to minimize the error between the targets (desired) output presented by
‘teacher’ and the computed output, to achieve better performance. However, in unsupervised
learning, there is no teacher present to hand over the desired output and the network therefore
tries to learn by itself, organizing the input of the problem.
Though NN architecture has been broadly classified as single layer feedforward networks,
multilayer feedforward networks, and recurrent networks, over the years several other NN
architectures have evolved. Some of the well known NN systems include backpropogation
network, perceptron, ADALINE (Adaptive Linear Element), associative memory, Boltzmann
machine, adaptive resonance theory, self-organizing feature map, and Hopfield network.
Neural network have been successfully applied to problems in the fields of pattern recognition,
image processing, data compression, forecasting, and optimization to quote a few.
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WORKING
The handwritten document is scanned using a scanner. The image of the scanned
document is processed using the program. Initially each line is separated. Then the different
words on each line are separated, and then each character in the word is isolated.
Then the individual isolated character is subjected to “Feature Extraction”. The
features are the distinguishing characteristics of the letters, which are used to classify the letters
into different classes. At the end of “Feature Extraction” every character has a Feature Vector
assigned to it. This Feature Vector is a set of numbers corresponding to the characteristics of the
letter.
Now this Feature Vector is given to the Neural Network. The Neural Network has
two phases of operation
-Training phase
-Testing phase
Training phase
In this phase the Neural Network is trained by using the Feature Vector which is
extracted from the characters. During training the network updates its weights according to the
input pattern. At the end of training phase, the neural network reaches a steady state where its
weights do not change. The weights attain a final value such that any pattern similar to those
samples which are presented in the training phase can be recognized.
Testing phase
In this phase, the Feature Vector of the character is given as input to the Neural
Network. The Neural Network processes these inputs and recognizes the character. During this
phase no weight change takes place.
These characters are then converted to a font recognized by a computer and then displayed.
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Features Extracted
Character height
(Tamil “ki”)
Fig 2 :Character height
Some characters are taller than the others. Hence, such characters which have a part lying above
a particular level fixed in the program can be distinguished from the other shorter characters.
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Characters having closed loops
(Tamil “a”)
(Tamil “zha”)
Fig 3 : Characters having closed loops
Some characters have closed loops, which can be filled using the MATLAB function “imfill”. If
the characters are divided into four quadrants, different characters show such loops in different
quadrants. Hence, when the loops are filled, the number of pixels in particular quadrants
increases. This can be used to group such characters with closed loops into different categories.
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Maximum Number of Horizontal Transitions
(Tamil “Ta”)
(Tamil “Na”)
Fig 4: Maximum Number of Horizontal Transitions
Here, a ‘transition’ refers to going from background to foreground and back to the background
once. When scanned horizontally, different characters show different number of maximum
transitions, based on which, different characters can be grouped into categories.
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Maximum Number of vertical transitions
(Tamil “ja”)
(Tamil “pa”)
Fig 5: Maximum Number of Vertical Transitions
Similar to horizontal transitions, when scanned vertically, different characters show different
characters show different number of transitions.
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Number of pixels in different quadrants
(Tamil “pa”)
(Tamil “Sha”)
Fig 6 : Number of pixels in different quadrants
Different characters show different pixel density in different quadrants. This feature can be used
to differentiate amongst the characters.
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MODELS OF NEURAL NETWORKS UNDER CONSIDERATION
Bidirectional Associative Memory
Figure 7: Bi-directional associative memory
Bi-directional associative memory is a hetero-associative recurrent neural network consisting of
two layers. The net iterates by sending a signal back and forth. Between the two layers until each
neuron’s activation remains constant for many steps (i.e. till it reaches equilibrium). The net
associates a set of patterns by summing bipolar correlation matrices. The net can respond to input
on either layer.
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Neocognitron Model
The Neocognitron is powerful in its ability to recognize patterns despite translation, rotation,
distortion and changes in scale. It can accept two dimensional patterns like those imaged into the
retina and processes them in successive layers. It is a hierarchical net in which there are many
layers with sparse and localized pattern of connectivity between the layers. The Neocognitron is
based on supervised learning.
Each layer has units arranged in number of square arrays. Only very limited number of signals
are transmitted from one unit to other. The layers are arranged in pairs that is, there is an s-layer
followed by a c-layer.
The S arrays are trained to respond to particular pattern or group of patterns. All cells in simple
array plane respond to same pattern. Each simple cell is sensitive to a restricted area by input
pattern. These areas overlap to cover the entire pattern.
The C-cells combine the outputs from S-cells and simultaneously thin out the number of units in
each array. Complex cells serve to make the system less sensitive to position of patterns in input
field.
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Radial Basis Function Network

1

1

m
i
n

Radial basis function network can be used for approximating functions and recognizing patterns.
It uses Gaussian Potential functions. The Gaussian potential functions are also used in networks
called regularization networks.
The architecture of radial basis function network is a multilayer feed forward network. There are
n input neurons and m output layers along with the hidden layer that exists between the input and
output layer. The interconnection between the input and hidden layer forms hypothetical
connection and between the hidden and output layer forms weighted connection.
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APPLICATIONS

With help of our project images of text pages having TAMIL characters can be converted
into ASCII character files for editing, combining with other files.

There is an increasing demand to convert paper document to electronic versions. This
system provides bridge from printing back to electronic storage and editing.

Automatic sorting of mails in Post office.

In various offices there are many pages of printed data and storage of these printed data is
becoming increasing difficult. Also to catalogue and refer these things is difficult. In such
offices this system is useful.

As we have recognized these characters are based on images, this system can be used as
an interactive system for physically challenged such as a blind person. Modification is
required to generate sound files of recognized characters so that a blind person will be
able to understand data on page.

Digitization of hand-filled forms.

Digitization of books (manuscripts and printed).
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CURRENT STATUS
• A Program, for extracting few features has been developed.
• The following features have been considered in the program :
-
Characters having parts below the line.
-
Characters having part above the line.
-
Number of pixels in different quadrant.
-
Number of horizontal transitions.
-
Characters having closed loops.
• Only individual characters have been considered instead of a full document.
• Only characters written on a ruled page have been considered.
• Characters written with black ink on a white page have been considered.
• The following three models of neural network
- Neocognitron
- Radial Basis Function Network
- Bi-directional Associative Memory
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FUTURE WORK AND PLAN OF ACTION
•
•
•
•
NEURAL NETWORK – 5th January 2008 to 25th January 2008
-
Selection of the NEURAL NETWORK to be used.
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Training the NEURAL NETWORK.
NOISE CONSIDERATIONS – 28th January 2008 to 15th February 2008
-
Ink blots, scratched-out characters should not be recognized as characters.
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Getting satisfactory results with Blurred images.
REDUCTION OF LIMITATIONS – 20th February 2008 to 10th April 2008
-
Characters not written on a ruled page should be recognized.
-
Reduction in the size limitations of the characters.
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Numerical characters should be recognized.
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Separation and recognition of accidentally joined characters.
ORIENTATION CONSIDERATIONS – 15th April to 25th April 2008
-
Slanting characters should be recognized.
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BIBLIOGRAPHY
1. Gonzalez , Woods ,Eddins ‘Digital Image Processing Using MATLAB’
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Fundamentals (Page no.-12)
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Histogram (Page no.-76)
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Special filtering (Page no.-89)
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Morphological image processing (Page no.-334)
2. S.N.Shinvanandum, S.Sumathi, S.N.Deepa ‘Introduction To Neural Networks Using
MATLAB’
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Bi-directional Associative Memory (Page no.-153)
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Neocognitron (Page no.-318)
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Radial Basis Function Network (Page no.-212)
3. James A Freeman, David M. Skapura ‘Neural Networks’
-
Neocognitron (Page no.-373)
Papers referred
-
Optical Character Recognition for printed Tamil text
using Unicode by Seethalakshmi R., Sreeranjani T.R., Balachandar T.
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Handwritten Character Recognition By Miguel Po-Hsein Wu.
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Hand-Printed Character Recognizer using Neural Networks By: Shahzad Malik.
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APPENDIX
Tamil alphabet
Vowels and vowel diacritics
Non-standard consonant-vowel combinations
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Consonants
Sample text in Tamil
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