mixed conjunct consonants recognition by using soft ...like as fuzzy neural hybrid system, fuzzy...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 23 (2017) pp. 13405-13415

© Research India Publications. http://www.ripublication.com

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Mixed Conjunct Consonants Recognition by using Soft Computing based

FNHS through NI-Vision Assistant

Santosh Kumar Henge1

Department of Computer Science, University Campus College, Kakatiya University, Hanamkonda, Warangal District, Telangana State, India.

Dr B.Rama2

Assistant Professor, Department of Computer Science, University Campus College, Kakatiya University, Hanamkonda, Warangal District, Telangana State, India.

1Orcid: 0000-0003-1884-9945, IDsScopus Author ID: 57193696079

Abstract

Soft Computing technology represents the improved versions

of intellectual architectures such as artificial intelligence

based neural networks, fuzzy control system, genetic

algorithms and these individual approached are blended

together to fabricate the influential intellectual hybrid systems

like as fuzzy neural hybrid system, fuzzy support genetic

algorithm and genetically support neural approach to convert

their individual drawbacks into features. In OCR approach,

many researchers are proposed various traditional cum

advanced approaches to recognize the individual characters,

number, consonants. Some researchers are designed

methodologies to recognize the conjunctives with their

consonants. But in real time scenario, machines can face the

difficulty to classify the input characters into consonants and

conjunctives, and especially identify the mixed connective

consonants with their exact correlated connectives especially

when they mixed with pre cum post located consonants.

This paper is proposing layered methodology to solve the

complex problems in mixed conjunct consonant OCR by

using the intellectual fuzzy based neural hybrid systems.

Performing the accurate categorization of input text into

concern individual consonant and conjunctives or vowel

composed consonant by measuring the correlated connectives

of each consonant to form the full text and identifications of

end-points where the connectives of vowels are mixed with

consonants. The input text smoothing, erosion, interpolation,

normalization, various stroke recognition variations,

classification and identification of mixed conjunct consonant

based recognition problems has been reduced through neural–

fuzzy–neural hybrid system through the NI-Vision Assistant

technologies.

Keywords: Soft Computing (SC), Text-Stressing-Blows

(TSB), Composite-Text (CT), NI-Vision Assistant (NIVA),

Input-Text-Normalization (ITN), Input-Text-Smoothing

(ITS), Input-Text-Interpolation (ITI).

INTRODUCTION

The In present days the traditional computing system based

approaches are successfully replaced with the hybrid soft

computing (SC) approaches by holding the complementary

merits and best practices to stimulate their appliance in each

environment [2]. SC hybrid based systems are sparkling

explore innovation of modern computational aptitude with the

growth of future invention of intellectual systems [1] and it is

an aggregation of growing methodologies intend at employing

the tolerance-ability for vagueness, partial truth towards to

attain heftiness, suppleness and low cost [10]. Most of many

Indian languages based text has composed with the similar

kind character set flow by holding the various key strokes.

Designing optical character recognition (OCR) engine

approached framework for one Indian language serves as a

supporting framework for other Indian language [8]. Brahmi

script is the main base for all the Indian languages. The

Brahmi script is a phonographic style of writing system. In

this system almost of all symbol forms are directly or

indirectly correlated to the phonemes of the language in which

it is written and also the characters processed with their

descendants and conjunctives. This type of mixed form of text

variations has been called as composite-text (CT). And also,

sometimes the individual consonants can mix with not only

with each of their correlated vowels of the handwritten system

but also with other consonants to outline the language

ligatures.

Researchers are proposed and designed the various OCR

approaches for handwritten and printed characters recognition.

The handwritten character recognition (HCR) is more

complex than the printed character recognition (PCR) because

of their internal stroke rate variations, flow and placement of

characters. Some of the methods have been proposed for the

handwritten character recognition, such as stochastic based

representation [22], structure oriented representation [19-20],

learning cum training environmental based approaches [21],

motor based method [23], support vector machine (SVM) and

neural network based approaches (NNbA). The learning cum

training environmental based approaches is inward extensive

consideration for HCR and pattern recognition (PC) based

problems. The NNbA has attained the high success rate

comparatively with other supporting survive models in many

recognition areas. The SVM has attained the rational

simplification based accuracy for recognizing the handwritten

based numeric recognition [26], Arabic character recognition

mailto:[email protected]

mailto:[email protected]



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[24], Roman character recognition [25] and Thai character

recognitions [27]. Every approach has its own techniques,

achievements, success rates and lagging issues. In this paper is

on the designing the system for off-mode printed and

handwritten character recognition of Telugu south Indian

language scripts by using the fuzzy neural hybrid system

through the NI-Vision Assistant (NIVA). An artificial based

neural network was pioneer by the McCulloch and Pitt based

on the surveillance that the human-brain consists of plentiful

interrelated neurons encapsulating the rarest thing in this

humankind - human based intelligence [12]. The major benefit

of NN is over conventional computers lies are in its high level

parallelism [11]. It is attractive extensively conventional

technology that the advent of NN will open new sympathetic

into how to make things easier programming and algorithm

invent for a given end of problems [5]. NN can also deriving

its own limitations like as the designing neural based

networks, extensive training periods, and opportunity of over-

fitting ratios, but finally it will effectively executes all

operations and forms the excellent outcomes by taking a

more care in right mode.

Figure 1: The computational intelligence based systems

derives many application areas in complex domains and

concern areas.

A blended environment of fuzzy logic controller (FLC) and

neural based network (NbN) practices are measured as single

amongst the list of methods [6]. By taking into consideration

of these two advancements independently, each of NbN based

and FLC inference based systems are holding its own self

qualities. The learning ability of NbN is best fit for structures

based function approximations. FLC inference rule based

systems are utilized for enhancing the decision making of

knowledge representations along with the NbN justification

potentials. The blended hybridization of soft computing based

fuzzy neural hybrid system (FNHS) is used to defeat their

individual limitations and it is imitated cum implicates the

human based thoughts and actions [7][14]. This combinational

hybridization intelligent techniques are attained the high

success rate in complex domains of applications in modern

and traditional literature [8] either when fuzzy set theory is the

heart of such a system, or when the neural mechanism is the

dominant component in the architecture. This blended

hybridization system will less evident and significant, relating

to dissimilar FLC or NbN based arrangements such as self-

systematize-maps or radial-based-operations [4].

OBSERVED COMPLEX PROBLEMS IN MIXED

CONJUNCT CONSONANTS OCR In OCR approach many researchers are proposed various

traditional cum advanced approaches to recognize the

individual characters, number, consonants. Some of them are

designed some methodologies to recognize the conjunctives

with their consonants. Some of the researchers are proposed

an overlapping-bounding-box based approach with the

implementation of convinced choice of edging margins for

segmentation of Telugu touching conjunctive consonants with

their vowels. This overlapping-bounding-box based method

was derived for recognizing the single conjunctives and it is

text-size based box advance. The detection of two fonts with

having touchy connection-form has expressed [15-16] and

tested with only touching of two basic characters [14]. But in

real time scenario, machines can face the difficulty to classify

the input characters into consonants and conjunctives, and

especially identify the mixed connective consonants with their

exact correlated connectives especially when they mixed with

pre cum post located consonants. The important major

complexity is to in Indian language HCR for differentiate

among the writing variations with the different text stressing

blows of single writer and same type of text stressing blows

by the different writers along with the negligible deviation in

comparable texts in the scripted language.

1 2 3 4 5 6 7

Figure 2: Telugu conjunctive consonant: first two consonants

formed with left-sided conjunctives, third consonant formed

with top cum bottom positioned conjunctives, fourth and fifth

consonants formed bottom positioned conjunctives with sub-

conjunctives, sixth and seventh consonant formed with left-

sided space-allocated conjunctives.

CIbS application areas in complex domains

Decision-making-process

Diagnosis-retrieval

Supervision-un-supervision

Computing-modelling

Data-analysis

Control based engineering,

Data analysis and Function approximation

Monitoring and diagnosis of complex dynamic

systems, chaotic domains and time-series data,

with a special emphasis on economic or

financial problems and electromechanical

based devices and systems

Numerous medical diagnosis problems

Managerial decisions and strategic decision-making

CIbS techniques Applied Areas

Computational Intelligence based Systems

(CIbS)



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Figure 3: Same conjunctive consonant formed with different handwritten key-strokes by the different writers

Major Objectives



using the intellectual fuzzy based neural hybrid systems.

Performing the accurate categorization of input text into

concern individual consonant and conjunctives or vowel

composed consonant by measuring the correlated connectives

of each consonant to form the full text and identifications of

end-points where the connectives of vowels are mixed with

consonants. The input text smoothing, erosion, interpolation,

normalization, various key-stroke recognition variations,

classification and identification of mixed conjunct consonant

based recognition problems has been reduced through neural–

fuzzy–neural hybrid system through the Lab View

technologies.

METHODOLOGY IMPLICATIONS THROUGH

FUZZY NEURAL HYBRID SYSTEM (FNHS)

The combinational framework of FNHS will implicate the

working mechanism to learn all the parameters from FLC

system. The FNHS system can executes the inference based

operations based on the FLC fuzzification rules by taking the

help of prior-predefined existing knowledge.

The layered environmental FNHS, The human-like reasoning

style of FLC will compose linguistic replica and fuzzy-sets

include a hug set of If-Then implicated fuzzy based rules.

FNHS derives the trained data-set has been formed from the

n-dimensions of functions and also it derives the inaccuracy-

computing-module to progress the learning statements when

the faults has been considered. The membership functions are

primarily defined, activate the generated parameters from the

membership functions and the training-learning process is

performed when it has required for the further execution [6].

Under the training process, the conditional based if-then-else

fuzzy inference rules are applied for constructing the domain

based expert process and for the future extraction. The NbN is

re-applied when the execution procedure has identifies the

poor training data-set, its re-initiated for another modified

training data-set. The classifications cum identification

process will starts by sort out the total text into two different

types of recognition forms such as normal consonants and

conjunct consonants.

The transformation futures of FNHS represents the inputs as

neuron based layers, converts it into fuzzified based set inputs,

and then apply the fuzzification sequence practices with the

wanted fuzzy rule based knowledge to the concern fuzzy input

sets and finally to produce the required output through

concern responses [7]. The derived combinational mixed-

version of FNHS based inference rules invents the technical

source to solve complex problems connective conjunct

consonants and numerals [14]. FNHS handle all divergence of

rational, numeric and linguistic data set values. Expert based

knowledge is easily adaptable by using the NbN and with

advancement of supplementary rules of fuzzification

membership functions to accomplish the convinced

conditions. It generates the realistic, accurate outcomes and

saves the times. As shown in the fig. 4.

ʃ (P(X))

Best outcomes

Normalization-

Input samples produced

based on the scaling factors

Fuzzification

Defuzzification

Rule-based

Inference Method

Operations are

executed under the

controlled of physical

device

Y1..n

ʃ (Q(Y))

X1...n

Basic input

values to the

system

Fuzzy System – estimated calculation

Component feeler used

for inaccuracy

calculations

Normalization-

Re-sampling the un-

recognized output

with new scaling

factors

Fuzzy-based data

samples generation Rules and group generations based

on the Fuzzification Regulation

Figure 4: Architecture of Fuzzy Neural Hybrid System with their concern internal Inference Rules [14].



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The rough input values will converted into layered

approached neuron base inputs such as X(i1-in), then these

inputs altered into fuzzy based input values to generate the

fuzzy sets. Then the fuzzification method has implemented

with fuzzy based inference rules such as ʃ (P(X)) for

previously collected data units, then the hug fuzzy set has

been formed with concern data sets A(i1-in) with help of

fuzzification membership functions cum derived rules and it

generates the values of ʃ (Q(Y)). In next stage, the linguistic

values such as Y1-Yn has been generated based set-based-

fuzzy-inference-rules. Finally the output responses (B) will be

produced once the linguistic-values satisfy the required-input-

linguistic values. If it is not satisfied or when it detects any

unrelated output symbol or identification-error then the

normalization progression will be implicated to concern

output linguistic values to avoid the redundancy based errors,

this process repetitive until the identifier detects any

overlapping recognition or slot correction based errors [14].

Figure 5: FNHS based methodology with fault calculation

mechanism implicating with passing through the concern

layers approach [7].

Pre-processing of input knock data

The text-stressing-blows (TSB) is pre-processed by using

three major steps, such as input-text-normalization, input-text-

smoothing and input-text- Interpolation as explained in the

following subsections [8].

1) Input-Text-Normalization (ITN): Most of characters

will be formed by various text-stressing-blows and

some TSB enhanced with the placement of top and

bottom of the original consonant. The original

consonant has been placed with one-line-space based

on the size of text has been formed by the composer.

Here the entire text has been classified by the three

line-spaces or layers such as the middle, top and bottom

line-spaces. The main and important line-space called

as middle-line-space which consist the main consonant,

the top-line-space and bottom-line-spaces will consists

the conjunctives and vowels. The middle-line-spaced

main consonants will derive largest TSB that crop-up in

the main part of the text and scaled down by its height

value. The pre-text, post-text and size of the text is

worn for normalization process if is very difficult to

recognize the TSB.

2) Input-Text-Smoothing (ITS): The input text

smoothing process can be applied once finished the

generalized ITN process when the TSB is motionless

piecewise linear with the line based fragments

connecting the points along its concern curve motion.

The Gaussian membership based filters has been best

applicable for providing the smoothing the input based

characters. Here different parameters has been

considered and applied in Gaussian membership based

filters depends on the size of the text, size the function

has been retrieved. In FLC system, the fuzzy based sets

can be build with help of the membership function for

representing each input value with the fuzzy

implicated real-numbers based set intervals 0’s and 1’s

such as [0, 1]. These fuzzy implicated set-intervals are

mentioned as membership based degrees or grades.

This function of memberships formed as Gaussian,

triangular and trapezoidal. A triangular membership is

described by a triplet (x, p, y), where “p” is the modal

value, x represents right boundary and y represents left

boundary. The Gaussian implicated membership

functions and trapezoidal implications are shown in

Fig. 6.

With 0 for any k ϵ { 1, 2,…….n}

Figure 6: The Gaussian implicated membership functions

represented for and trapezoidal implications

Bell Curve sets of Gaussian give fuzzy system with higher

quality by basic learning rules. The variables of bell curve can

tune by using these learning rules. The following equation

represents the function of Gaussian.

Figure 7: Sample data representation with various cost driver

implicated by using Membership Function of Gaussian. Here

Ci represents as center and σi represents width of the ith

fuzzy set respectively.

Layer-1

Layer-2 Layer-3 Layer-4 Layer-5

Membership degree



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The fuzzy logic based sets implicated with linguistic values

with concern membership function μ Gaussian shaped has

shown in Fig. 6. The fuzzy sets can be represented by

linguistic values, for each factor of interest it may be character

intensity, orientation, layout and alignment.

3) Input-Text-Interpolation (ITI): The input text

interpolation process can be applied after the ITS where

the smoothing TSB based text is interpolated based on

the starting cum ending points of the concern

consonants and their correlated conjunctives along with

mentioned space gap which implicated by the curve

variations. Each consonant has derived two end-points,

one as starting-point and ending-point. Sometimes

consonant derives four end-points if it formed with

concern conjunctive either in top-line-spaced or

bottom-line-spaced, two end-point belongs to main-text

considered as middle-line-spaced text and another two

end-point belongs concern conjunctive. Sometimes

consonant derives eight end-points once it has formed

with top-line-spaced and bottom-line-spaced

conjunctives along with the middle-line-spaced text.

The granules of ‘x’ play the task of fuzzy actions and

the Pi is their granular probabilities [29]. It should be

pointed out that in the case of probability distributions

it is necessary to reinitiate between ‘I’ granular

probability distributions and granule-valued probability

distributions [35][30] fig. 9 and fig 10. It should be

noted that there is a connection between the concept of

a granular probability distribution and the notion of

perfilieva transform [31]. An instance of a granule-

valued distribution is a random set. There is a close

connection between granule valued distributions and

the Dempster–Shafer theory of evidence [32] [33] [34].

Figure 8: Function represented with granulation based

implications such as S as small, M as medium and L as large

for generating the fuzzy sets

The granuland of f, f viewed as a synopsis of f.

Figure 9: Probability distribution based on Interpolation and

granulation

Figure 10: General-granular versus granule-based-valued

distributions

Decision Making State (DMS) of FNHS

The DMS of input text can be executed with help of four

various implications by taking the support of AI based neural

networks. Originally the input-text will classified into three

line-spaces like as Middle-line-spaced text (MLST), Top-line-

spaced text (TLST) and Bottom-line-spaced text (BLST). The

neural networks based layer approach can be applied for

distributing the considered input text. The MLST can be

considered as neural networks based layer1, TLST considered

as neural networks based layer2, BLST considered as neural

networks based layer3 and also there some other hidden

neural networks layers can be considered if there is sub

sequences of main consonants and their conjunctives. Here the

classification process can be done with help of four methods

such as Middle-line-spaced text (MLST); Middle-line-spaced

text (MLST) with Top-line-spaced text (TLST); Middle-line-

spaced text (MLST) with Bottom-line-spaced text (BLST);

Middle-line-spaced text (MLST) with Top-line-spaced text

(TLST) and Bottom-line-spaced text (BLST).

In this evolutionary research, the hybrid fuzzy neural

invention model is proposed by using Gaussian based

functions to deal with basic linguistic data values and text

image with layered approach analysis, and to generate

inference rules of fuzzy membership functions and

corresponding responses for further processing in neural

hybrid fuzzy system, the membership functions primitives

have been added to find form a character, which is part of the

layered based dictionary. The finally the character is to be

recognized cum tested with layered based dictionary

containing origin characters by using proficient methodology.



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Table 1: Input text implicated through AI based neural networks with their layer implication, origin of the text and fuzzy based

implicated inference rules.

S.No. AI based Neural

Networks

Layer Implication Base of Text Fuzzy based

Function

1 Layer1 (L1) Middle-line-spaced text

(MLST)

Original base Consonants (ObC) ʃ (1, 0.0, 0.0)

2 Layer2 (L2) Top-line-spaced text (TLST) Vowels Type TLST placed

Conjunctives (VTTLSTC)

ʃ (0, 1.0, 0.0)

3 Layer3 (L3) Bottom-line-spaced text (BLST) Vowels Type BLST placed

Conjunctives (VTBLSTC)

ʃ (0, 0.0, 1.0)

4 Hidden Layer1 (HL1) Only MLST without TLST and

BLST

MLST without TLST & BLST ʃ (1, 0.0, 0.0)

5 Hidden Layer2 (HL2) MLST with TLST ObC with VTTLSTC ʃ (1, 1.0, 0.0)

6 Hidden Layer3 (HL3) MLST with BLST ObC with VTBLSTC ʃ (1, 0.0, 1.0)

7 Hidden Layer4 (HL4) MLST with TLST and BLST ObC with VTTLSTC &&

VTBLSTC

ʃ (1, 1.0, 1.0)

8 Hidden Layer5 (HL5) MLST with TLST+Sub- TLST

and BLST

ObC with VTTLSTC && Sub-

VTTLSTC && VTBLSTC

ʃ (1, 1.1, 1.0)

9 Hidden Layer6 (HL6) MLST with TLST and

BLST+Sub-BLST

ObC with VTTLSTC &&

VTBLSTC && Sub-VTBLSTC

ʃ (1, 1.0, 1.1)

10 Hidden Layer7 (HL7) MLST with BLST+Sub-

BLST+Sub.Sub-BLST

ObC with && VTBLSTC &&

Sub-VTBLSTC && Sub-Sub-

VTBLSTC

ʃ (1, 1.0, 1.1.1)

Experimental Design and Simulation Results

OCR is the process by which a NI machine vision application

interprets the characters and symbols in assessment images.

The OCR Training based interface has implicated for training

and learning process to classify the text based on their text-

orientation, text-alignment, text-normalization based on the

neural networks layers with help of fuzzy based inference

rules to recognize the concern characters.

Figure 11: Telugu consonants and conjunctives implicated

with interface based learning set

Figure 12: Various involved stages in pre-processing input

text-stressing-blows (TSB) of Telugu handwritten characters,

the stages are Input Text Normalization (ITN), Erosion, Input

-Text- Smoothing (ITS) and Input Text Interpolation (ITI)



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Figure 13: Telugu consonants and conjunctives implicated with concern converted another form of codes

Figure 14: Telugu consonants and conjunctives implicated with concern converted another form of codes

Figure 15: Final results of Telugu consonants and conjunctives along with their converted recognized symbol code



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Table2. Input text implicated with another form of text with concern classification score, fuzzy logic inference set along with

recognition success rate.

Symbol

Formation

Classification

Score

Fuzzy Logic

Inference Set Left Top Width Height

Recognition

Success Rate in %

n 710 ʃ (1, 0.0, 0.0) 55 39 28 16 71 %

€ 809 ʃ (1, 0.0, 0.0) 100 39 28 16 80.9 %

‚ 976 ʃ (1, 0.0, 0.0) 145 39 31 19 97 %

‡ 992 ʃ (1, 1.0, 0.0) 193 29 39 26 99 %

– 785 ʃ (1, 1.0, 0.0) 249 33 25 22 78.5 %

} 989 ʃ (1, 1.0, 0.0) 291 33 41 22 98.9 %

TT 985 ʃ (1, 0.0, 0.0) 350 39 47 16 98.8 %

TÖ 996 ʃ (1, 1.0, 0.0) 414 39 60 16 99 %

m 924 ʃ (1, 0.0, 0.0) 491 33 23 22 92.6 %

@ 803 ʃ (1, 1.0, 0.0) 531 30 23 25 80 %

× 937 ʃ (1, 0.0, 0.0) 55 99 23 16 93 %

ÿ 915 ʃ (1, 0.0, 0.0) 95 99 24 16 91 %

z 966 ʃ (1, 1.0, 0.0) 136 92 24 23 96 %

W 970 ʃ (1, 1.0, 0.0) 177 94 29 21 97 %

ng 995 ʃ (1, 0.0, 0.0) 223 99 49 16 99 %

n^g 988 ʃ (1, 0.0, 0.0) 297 99 34 16 98.5 %

£ 797 ʃ (1, 1.0, 0.0) 55 161 24 26 79.7 %

K 980 ʃ (1, 0.0, 1.0) 96 171 25 21 98.2 %

· 480 ʃ (1, 1.0, 0.0) 138 161 23 26 48.6 %

· 990 ʃ (1, 1.0, 1.0) 176 160 35 33 99.2 %

¤ 566 ʃ (1, 0.0, 0.0) 228 170 31 21 57 %

á 871 ʃ (1, 1.0, 0.0) 276 161 26 26 87 %

ó 992 ʃ (1, 1.0, 1.0) 319 161 26 31 92 %

È 839 ʃ (1, 0.0, 0.0) 362 171 24 16 84 %

Á 992 ʃ (1, 1.0, 1.0) 403 160 41 33 99 %

v 958 ʃ (1, 0.0, 0.0) 462 171 24 17 96 %

³ 889 ʃ (1, 1.0, 0.0) 503 165 25 22 89 %

s 638 ʃ (1, 1.0, 0.0) 545 161 23 26 64 %

& 565 ʃ (1, 1.0, 0.0) 55 220 23 26 57.5 %

ó 750 ʃ (1, 1.0, 1.0) 96 220 23 31 75 %

D 980 ʃ (1, 0.0, 0.0) 137 230 24 16 98 %

Ô 832 ʃ (1, 1.0, 0.0) 178 220 25 26 84 %



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ó 771 ʃ (1, 1.0, 1.0) 220 220 24 31 78 %

< 890 ʃ (1, 1.0, 0.0) 261 220 24 26 89 %

ó 863 ʃ (1, 1.0, 1.0) 302 220 24 31 87 %

ƒ 831 ʃ (1, 1.0, 0.0) 343 220 23 26 84 %

| 789 ʃ (1, 1.0, 0.0) 383 219 23 27 79 %

/ 890 ʃ (1, 1.0, 1.0) 424 219 23 32 89 %

‹ 459 ʃ (1, 0.0, 0.0) 465 230 25 16 47 %

“: 465 ʃ (1, 1.0, 1.0) 507 222 28 29 46 %

T 961 ʃ (1, 1.0, 0.0) 549 220 34 26 96 %

j 993 ʃ (1, 1.0, 0.0) 63 280 44 26 99.3 %

s 868 ʃ (1, 1.0, 0.0) 124 280 23 26 86.8 %

\ 868 ʃ (1, 0.0, 0.0) 162 290 24 16 87 %

e 383 ʃ (1, 1.0, 0.0) 203 280 23 26 39 %

ø 994 ʃ (1, 1.0, 0.0) 243 280 27 26 99.4

d 216 ʃ (1, 1.0, 0.0) 286 279 23 27 22 %

Ÿ 984 ʃ (1, 1.0, 0.0) 326 279 27 31 98 %

Ÿ 878 ʃ (1, 1.0, 0.0) 367 279 36 27 89 %

ø 997 ʃ (1, 1.0, 0.0) 420 280 25 26 99.7 %

£ 998 ʃ (1, 1.0, 1.0) 461 280 25 37 99 %

n 66 ʃ (1, 0.0, 0.0) 502 290 27 16 66 %

CONCLUSION

The individual approaches of neural networks and fuzzy logic

control system are blended together to fabricate the influential

intellectual hybrid systems like as fuzzy neural hybrid system,

fuzzy support genetic algorithm and genetically support neural

approach to convert their individual drawbacks into features.

OCR is the process of moderating the human written cum

printed text into machine readable digitalized form.



using the intellectual fuzzy based neural hybrid systems. The

Input Text Smoothing, Erosion, Interpolation, Normalization,

various Stroke Recognition variations, classification and

identification of mixed conjunct consonant based recognition

problems has been reduced through neural–fuzzy–neural

hybrid system through the Lab View technologies.

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mixed conjunct consonants recognition by using soft ...like as fuzzy neural hybrid system, fuzzy...

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