11.[89-105]Development of a Writer-Independent Online Handwritten Character Recognition System Using Modified Hybrid

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Computer Engineering and Intelligent Systems www.iiste.org

ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)

Vol 3, No.4, 2012

89

Development of a Writer-Independent Online Handwritten

Character Recognition System Using Modified Hybrid

Neural Network Model

Fenwa O. D.*

Department of Computer Science and Engineering,

Ladoke Akintola University of Technology, P.M.B 4000, Ogbomoso, Nigeria.

*E-mail of the corresponding author: [email protected]

Omidiora E. O.



E-mail: [email protected]

Fakolujo O. A.




Ganiyu R. A.




Abstract

Recognition of handwritten characters has become a difficult problem because of the high variability and

ambiguity in the character shapes written by individuals. Some of the problems encountered by researchers

include selection of efficient feature extraction method, long network training time, long recognition time

and low recognition accuracy. However, many feature extraction techniques have been proposed in

literature to improve overall recognition rate although most of the techniques used only one property of the

handwritten character. This research focuses on developing a feature extraction technique that combined

three characteristics (stroke information, contour pixels and zoning) of the handwritten character to create a

global feature vector. A hybrid feature extraction algorithm was developed to alleviate the problem of poor

feature extraction algorithm of online character recognition system. Besides, this research work also

focused on alleviating the problem of standard backpropagation algorithm based on error adjustment. A

hybrid of modified Counterpropagation and modified optical backpropagation neural network model was

developed to enhance the performance of the proposed character recognition system. Experiments were


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performed with 6200 handwriting character samples (English uppercase, lowercase and digits) collected

from 50 subjects using G-Pen 450 digitizer and the system was tested with 100 character samples written

by people who did not participate in the initial data acquisition process. The performance of the system was

evaluated based on different learning rates, different image sizes and different database sizes. The

developed system achieved better performance with no recognition failure, 99% recognition rate and an

average recognition time of 2 milliseconds.

Keywords: Character recognition, Feature extraction, Neural Network, Counterpropagation, Optical

Backpropagation, Learning rate.

1. Introduction

The use of neural network for handwriting recognition is a field that is attracting a lot of attention. As the

computing technology advances, the benefits of using Artificial Neural Network (ANN) for the purpose ofhandwriting recognition become more obvious. Hence, new ANN approaches geared toward the task of

handwriting recognition are constantly being studied. Character recognition is the process of applying

pattern-matching methods to character shapes that has been read into a computer to determine which alpha-

numeric character, punctuation marks, and symbols the shapes represent. The classes of recognition

systems that are usually distinguished are online systems for which handwriting data are captured during

the writing process (which makes available the information on the ordering of the strokes) and offline

systems for which recognition takes place on a static image captured once the writing process is over

(Anoop and Anil, 2004; Liu et al., 2004; Mohamad and Zafar, 2004; Naser et al., 2009; Pradeep et al.,

2011). The online methods have been shown to be superior to their offline counterpart in recognizing

handwriting characters due the temporal information available with the former (Pradeep et al., 2011).

Handwriting recognition system can further be broken down into two categories: writer independent

recognition system which recognizes wide range of possible writing styles and a writer dependent

recognition system which recognizes writing styles only from specific users (Santosh and Nattee, 2009).Online handwriting recognition today has special interest due to increased usage of the hand held devices.

The incorporation of keyboard being difficult in the hand held devices demands for alternatives, and in this

respect, online method of giving input with stylus is gaining quite popularity (Gupta et al., 2007).

Recognition of handwritten characters with respect to any language is difficult due to variability of writing

styles, state of mood of individuals, multiple patterns to represent a single character, cursive representation

of character and number of disconnected and multi-stroke characters (Shanthi and Duraiswamy, 2007).

Current technology supporting pen-based input devices include: Digital Pen by Logitech, Smart Pad by

Pocket PC, Digital Tablets by Wacom and Tablet PC by Compaq (Manuel and Joaquim, 2001). Although

these systems with handwriting recognition capability are already widely available in the market, further

improvements can be made on the recognition performances for these applications.

The challenges posed by the online handwritten character recognition systems are to increase the

recognition accuracy and to reduce the recognition time (Rejean and Sargurl, 2000; Gupta et. al., 2007).Various approaches that have been used by many researchers to develop character recognition systems,

these include; template matching approach, statistical approach, structural approach, neural networks

approach and hybrid approach. Hybrid approach (combination of multiple classifiers) has become a very

active area of research recently (Kittler and Roli, 2000; 2001). It has been demonstrated in a number of

applications that using more than a single classifier in a recognition task can lead to a significant

improvement of the systems overall performance. Hence, hybrid approach seems to be a promising

approach to improve the recognition rate and recognition accuracy of current handwriting recognition

systems (Simon and Horst, 2004). However, Selection of a feature extraction method is probably the single

most important factor in achieving high recognition performance in character recognition system (Pradeep

et. al., 2011). No matter how sophisticated the classifiers and learning algorithms, poor feature extraction

will always lead to poor system performance (Marc et. al., 2001). In furtherance, Fenwa et al.(2012)


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developed a feature extraction technique for online character recognition system using hybrid of

geometrical and statistical features. Thus, through the integration of geometrical and statistical features,

insights were gained into new character properties, since these types of features were considered to be

complementary.

2. Research Methodology

The five stages involved in developing the proposed character recognition system, which include data

acquisition, pre-processing, character processing that comprises feature extraction and character digitization,

training and classification using hybrid neural network model and testing, are as shown in Figure 2.2.

Experiments were performed with 6200 handwriting character samples (English uppercase, lowercase and

digits) collected from 50 subjects using G-Pen 450 digitizer and the system was tested with 100 character

samples written by people who did not participate in the initial data acquisition process. The performance

of the system was evaluated based on different learning rates, different image sizes and different database

sizes.

2.1 Data Acquisition

The data used in this work were collected using Digitizer tablet (G-Pen 450) shown in Figure 2.3 . It has an

electric pen with sensing writing board. An interface was developed using C# to acquire data (character

information) such as stroke number, stroke pressure, etc from different subjects using the digitizer tablet. 26

Upper case (A-Z), 26 lower case (a-z) English alphabets and 10 digits (0-9) making a total number of 62

characters. 6,200 characters (62 x 2 x 50) were collected from 50 subjects as each individual was requested

to write each of the characters 2 times (this is done to allow the network learn various possible variations of

a single character and become adaptive in nature). This serves as the training data set which was the input

data that was fed into the neural network.

2.2 Data PreprocessingPre-processing is done prior to the application of feature extraction algorithms. Pre-processing aims to

produce clean character images that are easy for the character recognition systems to operate more

accurately. Feature extraction stage relies on the output of this process. The pre processing techniques used

in this research work is Grid Resizing.

2.3 Resizing Grid

From the interface that was provided, there wasnt any degree of measurement to determine how small/big

the input character should be. Hence, the character written is resized to a matrix size of 5 by 7, 10 by 14

and 20 by 28 for all input characters. This is used to get the universe of discourse which is the shortest

matrix that fit the entire character skeleton. The universe of discourse is measured to easily get a uniform

matrix size in multiple of 5 by 7, 10 by 14 and 20 by 28 respectively.

Any character measured smaller than the required size is considerably resized to the multiple of 5 by 7, 10

by 14 and 20 by 28 conversely any character measured larger than the required size will also be resized tothe multiple of 5 by 7, 10 by 14 and 20 by 28. This implies that rows and columns of Zeros are added or

subtracted from the resized image matrix to achieve the required multiple of 5 by 7, 10 by 14 and 20 by 28.

2.4 Feature Extraction Development

The goal of feature extraction is to extract a set of features, which maximizes the recognition rate with the

least amount of elements. Many feature extraction techniques have been proposed to improve overall

recognition rate; however, most of the techniques used only one property of the handwritten character. This

research focuses on a feature extraction technique that combined three characteristics (stroke information,

contour pixels and zoning) of the handwritten character to create a global feature vector. Hence, a hybrid

feature extraction algorithm was developed using Geometrical and Statistical features as shown in Figure


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2.4. Integration of Geometrical and Statistical features was used to highlight different character properties,

since these types of features are considered to be complementary.

2.4.1 The Developed Hybrid (Geom-Statistical) Feature Extraction Algorithm

The Hybrid feature extraction model adopted in this work is as shown in Figure 2.4. Stages of

development of the proposed hybrid feature extraction algorithm are as follow:

Stage 1: Get the stroke information of the input characters from the digitizer (G- pen 450). These include:

(i) Pressure used in writing the strokes of the characters

(ii) Number (s) of strokes used in writing the characters

(iii) Number of junctions and the location in the written characters

(iv) The horizontal projection count of the character.

Stage 2: Apply Contour tracing algorithm to trace out the contour of the characters:

Stage 3: Develop a modified hybrid zoning algorithm and RUN it on the contours of the characters:

Two zoning algorithms were proposed by Vanajah and Rajashekararadhya in 2008 for the

recognition of four popular Indian scripts (Kannada, Telugu, Tamil and Malayalam) numeral.

These were: Image Centroid and Zone-based (ICZ) Distance Metric Feature Extraction System

(Vanajah and Rajashekararadhya, 2008a) and Zone Centroid and Zone-based (ZCZ) Distance

Metric Feature Extraction System (Rajashekararadhya and Vanajah, 2008b). The Two Algorithms

were modified in terms of:

(i) Number of zones being used (25 Zones) as shown in Figure 2.1.(ii) Measurement of the distances from both the Image Centroid and Zone Centroid to the

pixels present in each zone.

(iii) The area of application (uppercase (A-Z), lowercase (a-z) English alphabet and digit (0-9)).

Few zones are adopted in this research but emphasis is laid on how to effectively measure the pixel

densities in each zone. However, pixels pass through the zones at varied distances that is why we measured

five distances for each zone at an angle of 200

.

Hybrid of Modified ICZ and Modified ZCZ based Distance Metric Feature Extraction Algorithm.

Input: Pre processed character image

Output: Features for classification and recognition

Method Begins

Step1: Divide the input image into 25 equal zones.

Step2: Compute the input image centroid

Step3: Compute the distance between the image centroid to each pixel present in the zone measured at

angle 200

Step4: Repeat step 3 for the entire pixel present in the zone.

Step5: Compute average distance between these points.


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Step6: Compute the zone centroid

Step7: Compute the distance between the zone centroid to each pixel present in the zone measured at angle20

0.

Step8: Repeat step 7 for the entire pixel present in the zone

Step9: Compute average distance between these points.

Step10: Repeat the steps 3-9 sequentially for the entire zones.

Step11: Finally, 2xn (50) such features were obtained for classification and recognition.

Method Ends

Stage 4: Feed the outputs of the extracted features of the characters into the digitization stage in order to

convert all the extracted features into digital forms.

2.4.2 Development of Hybrid Neural Network Model

In this research work, hybrid approach with serial combination scheme was adopted. Hybrid of modified

Counterpropagation and modified Optical Backpropagation neural network was developed as shown in

Figure 2.5. The architecture of the neural network with three layers is illustrated in Figure 2.5. The output

of ith

layer is given by (2.1) except the output layer which uses maxsoft function:

ai = logsig(w

ia

i-1+ b

i)

(2.1)

where i = (2, 3) and a0

= P, a1

= E where E is the Euclidean distance between the weight vector and

the input vector.

wi= Weight vector of i

thlayer

ai= Output of i

thlayer

b

i

= Bias vector for i

th

layer.

The input vector P is represented by the solid vertical bar at the left. The dimensions of P are displayed

as 35 x 1, indicating that the input is a single vector of 35 elements (i.e. the image size). These inputs go to

weight matrix W1, which has 86 rows (i.e. 86 neurons in the first hidden layer) and 35 columns. A

constant 1 enters the neuron as input and is multiplied by a bias b1. The net input to the transfer function

(Euclidean distance) in the Kohonen (hidden layer) is n1, which is given as the Euclidean distance

between the weight vector wiand the input vector P. The neurons output a

1 serves as inputs to the second

hidden layer. These inputs go to weight matrix W2, which has 86 rows (i.e.86 neurons in the first hidden

layer) and 35 columns. A constant 1 enters the neuron as input and is multiplied by a bias b2.

The net input to the transfer function (log sigmoid) in the second (hidden layer) is n2, which is the sum of

the bias b2 and the product W

2a

1. The output a

2 is a single vector of 86 elements and this serves as

inputs to the output layer a3. These inputs go to weight matrix W

3, which has 86 rows (i.e. 86 neurons

from the second hidden layer) and 62 columns (26 uppercase + 26 lowercase + 10 digits). The net input to

the transfer function (maxsoft) in the output layer is n3, which is the sum of the bias b3 and the productW

3a

2. The neurons output a

3 is a single vector of 62 elements and this serves as the final output of the

neural network.

This research work adopted the modified Counterpropagation network (CPN) developed by Jude, Vijila,

and Anitha (2010). The training algorithm involves the following two phases:

(i) Weight adjustment between the input layer and the hidden layer (Kohonen layer)

The weight adjustment procedure for the hidden layer weights is same as that of the conventional CPN. It

follows the unsupervised methodology to obtain the stabilized weights. After convergence, the weights

between the hidden layer and the output layer are calculated.


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(ii) Weight adjustment between the hidden layer and its output layer

The weight adjustment procedure employed in this work is significantly different from the conventionalCPN. The weights are calculated in the reverse direction without any iterative procedures. Normally, the

weights are calculated based on the criteria of minimizing the error. But in this work, a minimum error

value is specified initially and the weights are estimated based on the error value. Thus without any training

methodology, the weight values are estimated. This technique accounts for higher convergence rate since

one set of weights are estimated directly. It is this output that served as the input to the modified optical

backpropagation algorithm.

2.4.2.1 Modified Optical Backpropagation Neural Network

In the standard backpropagation, the error at a single output unit is defined according to Equation as:

o pk = (Ypk- Opk) .fo'

k(neto

pk)

(2.2)

where the subscript p refers to the pth training vector, and k refers to the kth output unit, Ypk is the

desired output value, Opk is the actual output from kthunit, then opk will propagate backward to update the

output-layer weights and the hidden-layer weights. In the Optical backpropagation (OBP), error at a single

output unit is adjusted according to Otair and Salameh (2005) as:

New o

pk= (1+e(Ypk - Opk )2

. fo'

k (neto

pk)), if (Y O) >= zero

(2.3a)

New o

pk= - (1+e(Ypk - Opk )2

. fo'

k(neto

pk)), if (Y O) < zero

(2.3b)

The error function defined in Optical Backpropagation (Otair and Salameh, 2005) earlier is proportional to

the square of the Euclidean distance between the desired output and the actual output of the network for a

particular input pattern. As an alternative, other error functions whose derivatives exist and can be

calculated at the output layer can replace the traditional square error criterion (Haykin, 2003). In this

research work, error of the third order (Cubic error) has been adopted to replace the traditional square errorcriterion used in Optical backpropagation. The Equation of the cubic error is given as:

o

pk= -3(Ypk Opk)2

. fo'

k(netopk)

(2.4)

The cubic error in Equation (2.4) was manipulated mathematically in order to maximize the error of each

output unit which will be transmitted backward from the output layer to each unit in the intermediate layer.

These are as shown in Equations (2.5a) and (2.5b) below:

Modified opk= 3((1+ e

t)2

. fo'

k(netopk)) If (Ypk Opk)

2>= 0

(2.5a)

Modified opk= -3((1+ et)2 . fo'k(net

opk)) If (Ypk Opk)

2


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(i) Error signal function(ii) Application areaWith the introduction of Cubic error function and Momentum, the modified Optical Backpropagation is

given as:

1. Apply the input example to the input units.

2. Calculate the net-input values to the hidden layer units.

3. Calculate the outputs from the hidden layer.

4. Calculate the net-input values to the output layer units

5. Calculate the outputs from the output units

6. Calculate the error term for the output units, using Equations (2.5a) and (2.5b)

7. Calculate the error term for the hidden units, through applying Modified opkalso

Modified h

pj = fh'j(net

hpj) .(

opk . W

okj)

(2.7)

8. Update weights on the output layer.W

okj(t+1) = W

okj(t) + W

okj(t) + ( . Modified

opk . ipj)

(2.8)

9. Update weights on the hidden layer.

Whji(t+1) = W

hji(t) + ( . Modified

hpj . Xi)

(2.9)

Repeat steps from step 1 to step 9 until the error (Y pk Opk) is acceptably small for each of the training

vector pair. The proposed algorithm as in OBP is stopped when the cubes of the differences between the

actual and target values summed over units and all patterns are acceptably small.

2.4.3 The Hybrid Neural Network Algorithm

This research work employed hybrid of modified Counterpropagation and modified Optical

Backpropagation neural networks for the training and classification of the input pattern. The training

algorithm involves the following two stages:Stage A: Performs the training of the weights from the input nodes to the Kohonen hidden node.

Step 1: Weight adjustment between the input layer and the hidden layer

The weight adjustment procedure for the hidden layer weights is same as that of the conventional CPN. It

follows the unsupervised methodology to obtain the stabilized weights. This process is repeated for a

suitable number of iterations and the stabilized set of weights are obtained. After convergence, the weights

between the Kohonen hidden layer and the output layer are calculated.

Step 2: Weight adjustment between the hidden layer and the output layer

The weight adjustment procedure employed in this work is significantly different from the

conventional CPN. The weights are calculated in the reverse direction without any iterative procedures.

Normally, the weights are calculated based on the criteria of minimizing the error. But in this work, a

minimum error value is specified initially and the weights are estimated based on the error value. The

detailed steps of the modified algorithm are given below.

Step 1: The stabilized weight values are obtained when the error value (target output) is equal to zero (or) a

predefined minimum value. The error value used for convergence in this work is 0.1. The following

procedure uses this concept for weight matrices calculation.

Step 2: Supply the target vectors t1to the output layer neurons

Step 3: Since ( t1 y1 ) = 0.1 for convergence

(2.10)


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The output of the output layer neurons is set equal to the target values as:

y1 = t1 0.1

(2.11)

Step 4: Once the output value is calculated, the sum of the weighted input signals. Thus without any

training methodology, the weight values are estimated. This technique accounts for higher convergence rate

since one set of weights are estimated directly.

STAGE B: Performs the training of the weights from the second hidden node to the output nodes.

1. Calculate the net-input values from the Kohonen to the second hidden layer units.

2. Calculate the outputs from the second hidden layer.

3. Calculate the net-input values to the output layer units

4. Calculate the outputs from the output units

5. Calculate the error term for the output units, using Equations (2.5a) and (2.5b)6. Calculate the error term for the hidden units, through applying modified

opkas in Equation (2.7)

7. Update weights on the output layer using (2.8)

8. Update weights on the hidden layer using Equation (2.9)

Repeat steps from step 1 to step 8 until the error (Y pk Opk) is acceptably small for each of the training

vector pair. The proposed algorithm as classical BP is stopped when the cubes of the differences between

the actual and target values summed over units and all patterns are acceptably small.

The description of notation used in the training procedure is as given below:

Xpi : net input to the ith input unit

nethpj: net input to thejth hidden unit

Whji: weight on the connection from the ith input unit tojth hidden unit

ipj: net input to thejth hidden unit

netopk: net input to the kth output unit

Wo

kj: weight on the connection from thejth hidden unit to kth output unit

Opk: actual output for the kth output unit

3. Software ImplementationThis section discussed the phases of developing the proposed character recognition system. All the

algorithms have been implemented using C# programming language and RUN under Windows7 operating

system on Pentium (R) 2.00GB RAM, 1.83GH Processor and Pentium (R) 4.00GB RAM, 2.13GH

Processor respectively. Different interfaces representing the phases of the system development are shown

from Figures 2.6 to 2.11.

3.1 Character Acquisition

Character acquisition interface as in Figures 2.7 and 2.8 displayed a drawing area to serve as a platform to

acquire characters from users. Any drawn character on this platform must be saved into the developed

systems database by clicking the Add Botton. It also captured number of strokes and pressure used in

writing a character. A total 6,200 character samples were acquired using G-Pen 450 as shown in Figure 2.2


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and stored in the database.

3.2 Network Training

Before the network can be trained, the network parameters such as the learning rate, epoch value, the quit

error and character image size must be specified in the Application setting interface. The next thing is to

specify the total number of the character to be loaded from the database for training. The database size can

be varied by specifying the maximum number of character to be selected from character category

(uppercase, lowercase and digit). This can be accomplished with the interfaces shown in Figure 2.9 and

Figure 2.10. On clicking the Training phase button, the neural network trains itself with alphanumeric

characters of A Z, a z and 0 9 by performing several iterations based on the error value, learning rate

and the epoch value stipulated for the Neural Network. Epoch increases from 1 to 10,000 and the Error

reduces from a particular value depending on the value specified by the user. The training is terminated

when either the epoch reaches 10,000 or the error reaches the minimum value specified. Network training

results such the training time, the epoch value were displayed as shown in Figure 2.10.

3.3 Recognition Phase

Recognition of the character can be accomplished by clicking the Data acquisition/ Recognition phase

botton. User is required to write a character and click the Recognize botton. The results such as the

detected character, recognition rate and recognition time will be displayed as shown in Figure 2.11.

4. Performance Evaluation

The performance metrics adopted in evaluating the developed system are: different learning rate parameters,

different image sizes, different database sizes and system configurations. The results are as given in Figure

2.12 to Figure 2.17. Figure 2.12 shows the graph of learning rate versus epoch. Learning rate parameter

variation has a positive effect on the network performance. The smaller the value of learning rate, the lowerthe value with which the network updates its weights. This intuitively implies that it will be less likely to

face the over learning difficulty since it will be updating its links slowly and in a more refined manner.

However, this would also imply more number of iterations is required to reach its optimal state. Figure 2.13

indicates the graph of image size versus epoch. The image size is directly proportional to epoch. Usually,

the complex and large sized input sets require a large topology network with more number of iterations and

this has an adverse effect on the recognition time. Figure 2.14 shows the graph of variation of database size

and recognition rates. Increase in database size has a positive proportionality relation to the recognition

rates due to the fact that network is able to attribute the test character to larger character samples in the

vector space. However, rate of increment in recognition rate with respect to database size is considerably

small. Moreover, it can be shown from Figure 2.15 that the more the dimensional input vector (image

matrix size), the less the recognition performance. Three different image sizes (5 by 7, 10 by 14 and 20 by

28) were considered; it was shown from the results that the higher the image size, the higher the percentage

of recognition, although, the rate of change was small. Furthermore, increase in image size also increasesthe recognition time.

System configuration is also another factor influencing the performance of the recognition system. Both the

training time and recognition time are measured in CPU (processor) seconds, the higher the speed of the

system (processor speed), the lower the training time and the recognition time. This is due to the fact that

more time will be used by 1.8GH system to reach the required epochs (iteration) than the system with

2.13GH speed. The result is as shown in Figure 2.16. Figure 2.17 is the graph showing comparison of

performance of the developed system with related works in literature. It can be shown from the graph that

the accuracy of the system developed by Muhammad et. al. (2005) is higher than that of Muhammad et. al.

(2006). This shows that the performance of Counterpropagation neural network is better than that of

Standard Backpropagation. The best recognition rate was achieved in the developed system with no


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recognition failure. This means that the developed system was able to recognise characters irrespective of

the writing styles.

5. Conclusion and Future Work

In this paper, we have been able to develop an effective feature extraction technique for the proposed online

character recognition system using hybrid of geometrical and statistical features. The hybrid feature

extraction was developed to alleviate the problem of poor feature extraction algorithm of online character

recognition system. However, a hybrid of modified counter propagation and modified optical

backpropagation neural network model was developed for the proposed system. The performance of the

online character recognition system under consideration was evaluated based on different learning rates,

image sizes and database sizes. The results from the study were compared with some works in the literature

using counterpropagation and standard backpropagation respectively. The results showed that

counterpropagation performed better than standard backpropagation in terms of correct recognition, falserecognition, recognition failure and the achieved recognition rates were 94% and 81% respectively. The

developed system achieved better performance with no recognition failure, 99% recognition rate and an

average recognition time of 2 milliseconds. Future work could be geared towards integrating an

optimization algorithm into the learning algorithms to further enhance the convergence of the neural

network.

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Riedmiller, M. and Braun, H. (1993): A Direct Adaptive Method for Faster Backpropagation learning the

PROP Algorithm, Proceedings of the IEEE International Conference on Neural Networks (ICNN), 1: 586-

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Otair, M.A. and Salameh, W.A. (2005a): Online Handwritten Character Recognition using an Optical

Backpropagation Neural Network, Issues in Informing Science and Information Technology, 2: 787-797.

Rajashekararadhya, S.V. and Vanaja, P.R. (2008a): Handwritten numeral recognition of three popular

South Indian Scripts: A Novel Approach:, Proceedings of the Second International Conference on

information processing ICIP: 162-167.Rajashekararadhya S.V. and Vanaja, P.R (2008b): Isolated Handwritten Kannada Digit Recognition: A

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Figure 2.1: Character n in 5 by 5 (25 equal zones)

Figure 2.2: Block Diagram of the Developed Character Recognition System

Figure 2.3: The snapshot of Genius Pen (G-Pen 450) Digitizer for character acquisition


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Figure 2.4: The Developed Hybrid Feature Extraction Model

a1

= E (W1

P),a2

= logsig (W2a

1+ b

2), a

3= Maxsoft (W

3+ b

3)

Figure 2.5: The Hybrid Neural Network Model


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Figure 2.6: Graphic user interface of the developed Online Character Recognition System

Figure 2.7: Acquisition of character A using 3 strokes


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Figure 2.8: Acquisition of character A using 2 strokes

Figure 2.9: Network parameter setting and loading data to be trained from the database

Figure 2.10: Network Training in progress

Figure 2.11: Result of recognition process displaying recognition status


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Figure 2.12: Graph showing the effect of variation in Learning Rate and Database size on Epochs

Figure 2.13: Graph showing the effect of variation of Image size and Database size on Epoch

Figure 2.14: Graph showing the effect of variation in Database size and Epoch on the Recognition Rate


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Figure 2.15: Graph showing the effect of variation in Image size and Database size on the Recognition Rate

Figure 2.16: Graph showing the effect of variation in system configurations and Database size on Epochs

CR = Correct Recognition; FR = False Recognition; RF = Recognition Failure

Figure 2.17: Graph showing performance evaluation of the developed system with related works


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11.[89-105]Development of a Writer-Independent Online Handwritten Character Recognition System Using Modified Hybrid