OCR-THE 3 LAYERED APPROACH FOR DECISION MAKING STATE AND IDENTIFICATION OF TELUGU HAND WRITTEN AND PRINTED CONSONANTS AND CONJUNCT CONSONANTS BY USING ADVANCED FUZZY LOGIC CONTROLLER

International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 3, May 2016
DOI: 10.5121/ijaia.2016.7303 23
OCR-THE 3 LAYERED APPROACH FOR DECISION
MAKING STATE AND IDENTIFICATION OF TELUGU
HAND WRITTEN AND PRINTED CONSONANTS AND
CONJUNCT CONSONANTS BY USING ADVANCED
FUZZY LOGIC CONTROLLER
Dr.B.Rama and Santosh Kumar Henge
Department of Computer Science, Kakatiya University, Warangal, India
ABSTRACT
Optical Character recognition is the method of digitalization of hand and type written or printed text into
machine-encoded form and is superfluity of the various applications of envision of human’s life. In present
human life OCR has been successfully using in finance, legal, banking, health care and home need
appliances. India is a multi cultural, literature and traditional scripted country. Telugu is the southern
Indian language, it is a syllabic language, symbol script represents a complete syllable and formed with the
conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical
than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and
post level of consonants. This paper proposes the layered approach methodology to recognize the
characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient classification of
the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system
controller to take the text in the form of written or printed, collected the text images from the scanned file,
digital camera, Processing the Image with Examine the high intensity of images based on the quality
ration, Extract the image characters depends on the quality then check the character orientation and
alignment then to check the character thickness, base and print ration. The input image characters can
classify into the two ways, first way represents the normal consonants and the second way represents
conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal
consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process
starts from middle layer, and then it continues to check the top and bottom layers. The recognition process
treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of
present base character otherwise treats as normal consonants. The post processing technique applied to all
three layered characters. Post processing of the image: concentrated on the image text readability and
compatibility, if the readability is not process then repeat the process again. In this recognition process
includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In
the process of development of the algorithm the pre-processing, segmentation, character recognition and
post-processing modules were discussed. The main objectives to the development of this paper are: To
develop the classification, identification of deference prototyping for written and printed consonants,
conjunct consonants and symbols based on 3 layered approaches with different measurable area by using
fuzzy logic and to determine suitable features for handwritten character recognition.
KEYWORDS
Optical Character Recognition (OCR), TopMiddleBottom Layer (TMBL) layer, BottomMiddleTop Layer
(BMTL), Hand written and printed character recognition (HWPCR), Artificial Neural Network (ANN).

24
1. INTRODUCTION
Optical Character Recognition is the method of digitalization of hand and type written or printed
text into machine-encoded form. OCR is the most active invention research area in the field of
image processing, character and pattern recognition. In present life OCR has been successfully
using in finance, legal, banking, health care and home need appliances. Character Recognition
classified into two ways, online and offline. Online character and pattern recognition method is
finer to their off mode counterparts in recognition of hand written characters due to the temporal
information available with the formal information. In Off-Line mode character recognition, the
written or printed document can be scanned as an image then it can be digitized then converted it
into machine readable form with different character reorganization algorithmic methodology.
Off-Line mode character recognition process is an active and effective research area towards to
development of new innovations, ideas and techniques that would improve recognition accuracy.
The OCR consists the different levels of processing methods like as Image Pre Acquisition,
Acquisition, Pre-processing, Segmentation, Post processing, Feature Extraction and
Classification. India is a multi cultural, literature and traditional scripted country. 18 official
scripted languages are formed and have many local regional languages in India. Telugu is the
official language of the southern Indian states of Telangana and Andhra Pradesh. Telugu is also
spoken in all over in Malaysia, Bahrain, Oman, Singapore, Fiji, UAE and Mauritius. Officially,
there are 10 numerals, 18 vowels, 36 consonants, and three dual symbols. Telugu is the Dravidian
composed language and it is the third most popular script in India. The Telugu script is closely
related to the Kannada script. In OCR, captured or scanned input image is active from number of
stages like Image Acquisition, pre-processing, processing, post-processing, segmentation, feature
extraction and classification to perform Optical Character Recognition. Scanned Images or
captured photographs taken as input for the OCR system in Image Acquisition stage. Pre-
processing is important and necessary to convert the raw data to correct deficiencies in the data
acquisition process due to limitations of the capturing device sensor. Pre-processing stage step
involves detect text stroke rate, binarization, normalization, noise removal and so on.
Segmentation is the process of dividing the individual cum grouped characters, separating line
spaces, words and mixed characters from scanned image. Feature extraction explores the exact
identification of the characters, can be considered as finding a set of features that define the shape
of the underlying character as precisely and uniquely as possible.
2. EARLIER WORK WITH OPTICAL CHARACTER RECOGNITION
ALGORITHMS
In the past and present invasion of OCR, many algorithms are designed for different ways of
character recognition processes such as Template Matching, Statistical Algorithm, Structural
Algorithm, Neural Network Algorithm and Support Vector Machine. Template Matching
Algorithm proposed only for the recognition of the typewritten characters. The Statistical
Algorithm, Structural Algorithm, Neural Network Algorithm and Support Vector Machine
proposed for recognition of both type and handwritten characters. Each algorithmic methodology
carries both advantages and disadvantages.
2.1. Neural Network Algorithm
An Artificial Neural Network is an innovative methodology for information processing. ANN is
inspired by the biological nervous system, such as the main system act like as brain and its inter-
connected nerve process data. ANN composes huge number of inter-connected neurons for
processing data cum elements working in harmony to solve the problems [25]. A neural network
is a powerful data modelling tool that is able to capture and represent complex input/output

25
relationships. Neural network algorithm activated and identifies the characters by boosting and
worm-up of trained neuron of the neural network. Feed forward Neural Network, Feedback
neural network and Self Organizing Map are the types of neural network. Neural network
algorithm especially works for new characters can be found when it can middle of recognition
process, and also it is a suitable.
2.2. Support Vector Machine
The Support Vector Machine (SVM) is a related to support vector networks and set of supervised
learning methods used for classification. Support vector machine algorithm activated and
discover the characters by scrutinise and mapping the given input information on with high
priority dimensional future apace and it can be determine a dividing hyper plane with maximum
and minimum margin data. SVM is robust, accurate and very effective even though when the
training samples and models are less and it can perform good result without adding prior data sets
and feed information.
2.3. Structural Algorithm
The initial idea behind the creation of structural algorithms is the recursive description of a
complex pattern in terms of simpler patterns based on the size and shape of the object [23]. This
structural algorithm activated and identifies by recognize compound component of the character.
Structural algorithm classifies the input patterns on the basis of components of the characters and
the relationship among these components. Firstly the primitives of the character are identified
and then strings of the primitives are checked on the basis of pre-decided rules [00]. Structural
pattern recognition is intuitively appealing because in addition to classification, this approach also
provides a description of how the given path constructed from the primitives: [24]. Generally a
character is represented as a production rules structure, whose left-hand side represents character
labels and whose right-hand side represents string of primitives. The right-hand side of rules is
compared to the string of primitives extracted from a word. So classifying a character means
finding a path to a leaf: [22]. This algorithm mainly uses the structural shape pattern of the
objects.
2.4. Statistical Algorithm
The purpose of the Statistical Algorithm is to determine and categorize the given pattern based on
the statistical approach like as pre planned made observations, measurement approaches and a set
of numbers prepared which is used to prepare a measurement vector [22]. Statistical algorithm
uses the statistical decision functions and a set of optimality criteria which to maximizes the
probability of the observed pattern given the model of a certain class.
Statistical algorithms activated and identifies by making the measurement and assumptions.
Statistical algorithm is based on three assumptions. Such as distribution of present cum future set,
sufficient statistics presented in each class and collection of pre-images to extract a set of features
which represents each distinct class of image pattern. The major advantage is, it works even when
prior data or information is not available about the characters in the training data.
2.5. Template Matching Algorithm
Template Matching Algorithm known as pattern matching algorithm. All basic characters and
symbols are pre-stored in the system, and it is system prototype that useful to classify, identifies
the characters by comparing two pattern matching symbols or images. Template matching is the

26
process of finding the location of sub image called a template inside an image. Template
matching algorithm activated and identifies by Comparing derived image features and templates:
[21]. It is easy to implements but it only works on the pre-stored fonts and templates.
3. GENERAL POINTS OF CLASSIFICATION OF THE CONSONANTS AND
MIXED CONJUNCT CONSONANTS
General points might be concentrated during the process of handwritten character recognition
when digitized input image is, such as handwritten characters.
• Image clarity, quality and range of the pen ink plotted.
• Written text stroke, clarity, and thickness of the text.
• Pen or pencil ink injecting ratio on the paper.
• Local variations, rounded corners, and improper extrusions
• Unreflective and relative size of the character.
• Some characters represents different shapes
• In the translation point of view, it can be entirely or partly and it represents
relative shift of the character.
• Individual and irrelative line pixels, segments and curves.
4. GENERAL TELUGU CHARACTERS, NUMBERS AND SYMBOLS
Figure 1. Telugu characters and numbers

27
Figure 2. Combinational vowels with consonants
Figure 3. Conjunct consonants
5. PAPER OBJECTIVES
The main objectives of this paper is To determine suitable features for decision making state and
identification of Telugu Written and Printed Consonants and Conjunct Consonants based on 3
layer approach with their orientation, alignment method. And also to develop the identification,
classification and deference prototyping for written and printed mixed and conjunct Consonants
characters with their orientation, alignment method by using fuzzy logic system.
6. WORKING METHODOLOGY
The scanned image page contains the different stroke levels of consonants, normal and mixed
conjunct consonants. In this research paper we are concentrated on different stroke levels of
consonants and mixed conjunct consonants. The scanned image has been processed under the
following processes.
• Hand written Text in image identification and detection.
• Hand written Text layout or orientation identification.
• Text classification of the text based on the orientation of the text
• Segmentation.
• Character processing applying the Gaussian Fuzzy process
• Post Processing Analysis.
In this implementation Fuzzy logic based neural network having the four methods (input,
fuzzification, inference and defuzzification) have been used. Fuzzification is the scaling of input
data to the universe of discourse (the range of possible values assigned to fuzzy sets). Rule
application is the evaluation of fuzzified input data against the fuzzy rules written specifically for
the system. Defuzzification is the generation of a specific output value based on the rule strengths
that emerge from the rule application. The Fuzzy based neural network rules can be applied for
the paper front and back side layered written and printed characters.
This paper implements the aadvanced ffuzzy llogic system controller collected the text in the
form of written or printed, collected the text images from the scanned file, digital camera,
Processing the Image with examine the high intensity of images based on the quality ration,
extract the image characters depends on the quality then check the character orientation and
alignment then to check the character thickness, base and print ration. The input image characters
can classify into the two ways, first as normal consonants and second as conjunct consonants,
first way represents the normal consonants and the second way represents conjunct consonants.
In this research, we classify input image written and printed as normal consonants and second as
conjunct consonants based on the 3 layer approach. The middle layer represents normal
consonants and the top and bottom layer represents conjunct consonants. Here recognition
process starts from middle layer, and then it will check the top and bottom layers, the recognition

28
process treat as conjunct consonants when it can detect any symbolic characters in top and bottom
layers of present base character otherwise treats as normal consonants as shown in the fig.5.
Capture the entire consonant or conjunct consonant characters from 3 layers Middle, Top,
Bottom(MTB) into single character or symbol. After conversion it into single symbolic character,
then the concern algorithmic methodology can be applied to identify the realistic name of the
character. In this methodology to classify the consonants and conjunct consonants proposed
concern algorithmic methodology can be applied in second level.
Figure 4. Examples and Tested Samples of handwritten Telugu characters with different modes.
Figure 5. Tested Samples of handwritten Telugu characters with layers to help the sensor to detect variation
of the written character type.
Top Layer
Middle Layer
Bottom Layer
Mixed Conjunct Consonant

29
We are applied the post processing technique to all 3 layer characters. Then after in Post
processing of the image, we are concentrated on the Image text readability and compatibility. If
the readability is not process then repeat the process again as shown data flow structure fig.6. In
this recognition process includes slant correction, thinning, normalization, segmentation, feature
extraction and classification.
Figure 6. Data Process and next level flow diagram in OCR process.
In the process of development of the algorithm the pre-processing, segmentation, character
recognition and post-processing modules were discussed. The main objectives to the development
of this paper are: To develop the classification, identification of deference prototyping for Written
and Printed Consonants, Conjunct Consonants and symbols based on 3 layer approach with
different measurable area by using fuzzy logic and to determine suitable features for handwritten
character recognition.
False
True, means quality ration is suitable to identify the page back sided text
and symbols
True
False
Extract image depends on the quality and it is ready
to read the machine.
Post processing of the image:
Machine can be read the characters
and text depends on the keystrokes
of text.
If the readability is
not process then
repeat the process
again.
Image text readability and
compatibility is sufficient to
read the text
Examine the intensity of images based
on the quality ration.
Processing the Image Stage 1
Stage 2
Take the text in the form of
written or printed.
Take the text images from the digital
camera/scanned file.
Page Front side text Quality <
Page Back side text Quality
Processing of the image - Stage 2
The character orientation, thickness, base, stroke, print
ration and alignment
Observed and verified. is checked.
Extract image depends on the quality.

30
7. IMPLEMENTATION
The Fuzzy logic was for the most part an object of skepticism and derision, in part because the
word ‘‘fuzzy” is generally used in a pejorative sense. Fuzzy logic is not fuzzy. Basically, fuzzy
logic is a precise logic of imprecision and approximate reasoning. More speciﬁcally, fuzzy logic
may be viewed as an attempt at formalization/mechanization of two remarkable human
capabilities. First, the capability to converse, reason and make rational decisions in an
environment of imprecision, uncertainty, incompleteness of information, conﬂicting information,
partiality of truth and partiality of possibility – in short, in an environment of imperfect
information. And second, the capability to perform a wide variety of physical and mental tasks
without any measurements and any computations [1].
The three elements required to realize a fuzzy system are fuzzification, rule application, and
defuzzification. Fuzzification is the scaling of input data to the universe of discourse (the range of
possible values assigned to fuzzy sets). Rule application is the evaluation of fuzzified input data
against the fuzzy rules written specifically for the system. Defuzzification is the generation of a
specific output value based on the rule strengths that emerge from the rule application.
In a realized fuzzy system, a microcontroller or other engine runs a linked section of object code
that consists of two segments. One segment implements the fuzzy logic algorithm, performing
fuzzification, rule evaluation, and defuzzification, and thus can be thought of as a generic fuzzy
logic inference engine. The other segment ties the expected fuzzy logic inputs and outputs, as
well as application-specific fuzzy rules, to the fuzzy logic inference engine[1] as shown in the
Figure 7.
Figure 7. Basic block diagram of Fuzzy System Crisp inputs and Outputs.
One may ask where and how fuzzy logic is implemented. here with the layer quadrants location
and three layered quadrants differentiation method for consonants and conjunct consonants with
the set of rules are known and which are the feeds for fuzzy logic controller [8 - 9] fuzzification
rules, these cases and conditions would be implemented as the if cases and for each individual
quadrant the processing action is to be done is written as the then-corresponding action Fuzzy-
neural network having the four layers (input, fuzzification, inference and defuzzification) have
been used.
7.1.Basic Configuration of a Fuzzy System

31
Fuzzy controller in a closed-loop configuration (top panel) consists of dynamic filters and a static
map (middle panel). The static map is formed by the knowledge base, inference mechanism and
fuzzification and defuzzification interfaces.
Figure 8. Fuzzy controller in a closed-loop configuration (top panel) consists of dynamic filters and a static
map (middle panel). The static map is formed by the knowledge base, inference mechanism and
fuzzification and defuzzification interfaces.
7.2. Fuzzy Sets
Fuzzy sets can be effectively used to represent linguistic values, such as low, young, and
complex. A fuzzy set can be defined mathematically by assigning to each possible individual in
the universe of discourse a value representing its grade of membership in the fuzzy set to a
greater or lesser degree as indicated by a larger or smaller membership grade. The fuzzy set is
represented as where x is an element in X and µA(x) is a membership function of set A which
defines the membership of fuzzy set A in the universe of discourse, X.
7.3. Fuzzy Membership Functions
A fuzzy set is characterized by a membership function which associates with each point in the
fuzzy set a real number in the interval [0, 1], called degree or grade of membership. The
membership function may be triangular, trapezoidal, Gaussian etc. A triangular membership is
described by a triplet (a, m, b), where „m‟ is the modal value, „a‟ and „b‟ are the right and left
boundary respectively. The trapezoidal membership function (shown in Figure. 9) is defined as
follows.

32
Figure 9. Trapezoidal Membership
Function for µ Z (xk, γ k)
Another fuzzy membership function that is often used to represent vague, linguistic terms is the
Gaussian which is called Gaussian membership function (shown in figure 10) is defined as
follows.
Figure 10. Gaussian Membership Function for µ Z(xk, γ k)

33
7.4. Gaussian Bell curve sets
Give richer fuzzy system with simple learning laws that tune the bell curve variance. The
Gaussian Function is represented by “(equation 1),”
Where Ci is the center of the ith
fuzzy set and is the width of the ith
fuzzy set.
Figure 11. Representation of DATA Cost driver using Gaussian Membership Function
We define a fuzzy set for each linguistic value with a Gaussian shaped membership function µ is
shown in Figure 10. We have defined the fuzzy sets corresponding to the various associated
linguistic values for each variable / parameter of interest it may be character intensity, orientation,
layout or anything.
In this research, a new fuzzy effort estimation model is proposed by using Gaussian function to
deal with linguistic data or text image with three layered quadrant position analysis, and to
generate fuzzy membership functions and rules for further processing the membership functions
Primitives have been added to find form a character, which is part of the lexicon. The word is not
said to be recognized till it is tested with lexicon containing root words with an efficient
algorithm [7]. The system working model is designed as shown in the flowchart figure 6, and the
process the recognition the consonants and conjunct consonants based the figure 8, figure 9,
figure.10 and figure.11 is repeated till the whole text is reached to get the clarity and in readable
and understandable.
8. DISCUSSION
The proposed algorithmic data flow presented to get the versatility in implementation while
constructing an OCR system; our proposal system can scan the text in different layered approach
with their different directions and orientation. There are many advantages of the proposed system.
First, when there are some feature parts which are related to knowing the decision making stages
and identification of mixed hand written letters and consonant character and the Second,
identification of low rate and low quality written mixed conjunct consonant text.
9. FUTURE SCOPE

34
The proposed 3 layered methodology approach planning to test and it can be implementing either
using the math tool or the LabVIEW VI GUI and MathLab. Our future work aims to improve the
classifier of the mixed and non-mixed conjunct consonants to achieve still better recognition rate
with our future proposal algorithmic methodology and also to improve the better recognition
procedure for low quality readable imaged Telugu mixed-conjunct-consonants.
Acknowledgements
I Dr B.Rama would like to thank everyone.
I Mr Santosh Kumar Henge would like to thank everyone.
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Authors
Dr B.RAMA, She received her Ph.D. Degree in Computer Science from Padmavati
Mahila Visvavidyalayam (Padmavati Women’s University), Thirupathi-India in the
year of 2009. She is working as Assistant Professor in Computer Science since six
years at Department of Computer Science, University Campus College, Kakatiya
University. She was the Chairperson, Board of Studies in Computer Science from
2013-15. She is having total 11 years of Teaching Experience in Engineering Colleges.
She is author or co-author around 20 scientific papers mainly in IEEE international
Conferences and International Journals. Her area of interest is Artificial Intelligence
and Data Mining.
SANTHOSH KUMAR HENGE, He received his M.Phil. Degree in Computer
Science from Periyar University, Salem – presently he is working as Associate
Professor in Computer Science. He is having very good International level teaching
experience. Previously, He worked in various countries Maldives, Libya, Oman and
Ethiopia with different level of positions. He was published more than 16 research
papers in International Journals and Conference Proceedings. He is doing his research
in the field of Artificial Intelligence-Neuro based Fuzzy System. His area of interest is
Artificial Intelligence and Data Mining.

OCR-THE 3 LAYERED APPROACH FOR DECISION MAKING STATE AND IDENTIFICATION OF TELUGU HAND WRITTEN AND PRINTED CONSONANTS AND CONJUNCT CONSONANTS BY USING ADVANCED FUZZY LOGIC CONTROLLER

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