Offline Marathi Handwritten Character Recognition … Marathi Handwritten Character Recognition...
Transcript of Offline Marathi Handwritten Character Recognition … Marathi Handwritten Character Recognition...
Offline Marathi Handwritten Character Recognition Using SVM Classifier
Based on Cloud Computing: Review
Sanjay S. Gharde Leena Y.Rane
Asst. Professor M.E.( CSE) IInd Year
SSBT COET, Jalgaon SSBT COET, Jalgaon
[email protected] [email protected]
Mob:-9422344964
Mob:-9405705760
Abstract
Handwritten recognition has been one of the active and challenging research areas in the field of image
processing. However, most of the current work in
these areas is limited to English and a few oriental
languages with single computer. Many systems and
classification algorithms have been proposed in the
past years on handwritten characters recognition in
various languages like English, Persian, Arabian and
Devanagari scripts also. Researchers had been
worked on Handwritten Devanagari characters by
applying different techniques on single PC but using
client and server recognize the Marathi handwritten
character based on cloud computing is challenging.
Previously researchers had been worked on online
Chinese characters based on cloud computing. So,
this research work has been conducted on offline
Marathi Handwritten character based on cloud
computing and this system is very important because person can access any data from anywhere. There
are various feature extraction techniques and
classifiers for recognition of Marathi characters but
Freeman chain code histogram and Gradient feature
extraction techniques are better because freeman
chain code approach is robustness to small variation
and easy to implement and the gradient technique
can be easily used to gray scale images and are
robust against image noise and edge direction
fluctuation. SVM classifier is better than KNN and
ANN classifier because of its complexity of training,
flexibility, classification accuracy and complexity
which is given bellow.
Keywords Devnagari Marathi Character Recognition, Off-line
Handwriting Recognition, Pre-processor, Feature Extraction, Image Classification.
1. Introduction
Offline handwriting recognition is one of the
challenging problems in pattern recognition. The
main challenges are wide variety of handwriting
styles, large varieties of pen-type, poor image quality
and a lack of ordering information of strokes. In the
this work a cloud-based recognition architecture is
presented. It has possible to be useful not only to end users, but also to researchers in the field. A cloud
infrastructure can support in the capture of
recognition history[1]. Additionally, such a model
has a number of other advantages: First, it allows the
writer to train the model only once and then use the
cloud with any device connected to the Internet.
Secondly, it gives the user access to various default
collections of training samples across different
alphabets, languages, and domains. Thirdly, it
provides a higher level of control over the
classification results and correction history. Further,
Cloud computing platform differentiate from the
traditional client server model. Cloud computing
offers the same super computer services over the
Internet and dynamically allocates distributed
computing resources. Cloud server can easily handle
service requests of multiple users simultaneously; Cloud computing system supports cross-platform
client terminals. Therefore, it would be more suitable
and easier for recognition the offline Marathi
handwritten character recognition[2].
2. Related Work
The first research work report on
handwritten Devnagari characters was published in
1977[3]. For Indian languages most of research work
is performed on firstly on Devnagari script and
secondly on Bangla script. U. Pal and B.B.
Chaudhury[4] presented a survey on Indian Script
Character Recognition. This
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This paer introduce recognition of many Indian
language scripts including Devnagari, Bangla, Tamil,
Oriya, Gurumukhi, Gujarati and Kannada.Bikash
Shaw, Swapan Kr. Parui, Malayappan Shridhar[5]
used a novel segmentation based approach for
recognition of offline handwritten Devanagariwords.
They also used Stroke based features for feature
vectors.In this paper Hidden Markov model is used
for recognition at pseudocharacter level.Sandhya Arora et al.[6] used intersection, shadow features,
chain code histogram and straight line fitting feature
extraction techniques and weighted majority voting
technique for combining the classification decision
obtained from different Multi-Layer Perceptron
(MLP) based classifier. They obtained 92.80%
accuracy results for handwritten Devnagari
recognition. Prachi Mukherji and Priti Rege[7]used
shape features and fuzzy logic to recognize offline
Devnagari character recognition. They segmented the
character into strokes using structural features like
endpoint, cross-point, junction points, and thinning.
Using tree and fuzzy classifier they classified the
segmented shapes or strokes as left curve, right
curve, horizontal stroke, vertical stroke, slanted lines
etc. They obtained average 86.4% accuracy.U. Pal,
Wakabayashi and Kimura also presented comparative study of Devnagari handwritten character recognition
using different features and classifiers[8]. They used
four sets of features based on curvature and gradient
feature extraction from binary as well as gray scale
images and compared results using 12 different
classifiers as concluded the best results 94.94% and
95.19% for features extracted from binary and gray
image respectively obtained with Mirror Image
Learning (MIL) classifier. They also accomplished
curvature features to use for better results than
gradient features for most of classifiers. Mallikarjun
Hangarge[9] investigate a texture for determining the
script of handwritten document image, Further, K
nearest neighbour classifier is used to classify 300
text blocks as well as 400 text lines into one of the
three major Indian scripts: English, Devnagari and
Urdu, Using morphological filters 13 spatial spread features are extracted. K. Roy, S. Kundu Das, Sk Md
Obaidullah[10] uses very simple and efficient
feature at component level. In this paper fractal-based
features, component based feature and Topological
features, series of classifiers were used. Accuracy of
the system is at present 89.48% on the test set
without rejection. In this approach gradient features
using Yan Gao, Lanwen Jin, Cong He, Guibin
Zhou[2] are extracted and then these features are
converted into 8 directional features. In chain code
histogram to character contour there are basically two
ways to determine the direction codes of contour
pixels. In one way, the order of contour pixels is
obtained by contour tracing, and then the chain codes
are calculated. And secondly we use the gradient
representation as the basis for extraction of features.
These algorithms require a few simple arithmetic
operations per image pixel which makes them
suitable for real-time applications. Ved Prakash
Agnihotri used Diagonal based feature extraction for
the handwritten Devanagari script. After that these
feature of each character image is converted into
chromosome bit string of length 378. In recognition
step using fitness function in which find the
Chromosome difference between unknown character and Chromosome which are store in data base[11].
Pankaj Kumawat , Asha Khatri , Baluram
Nagaria[12] used the Curve let transform and
Invariant moments for character recognition problem.
They compare the performance of HMM based
technique with combined HMM- SVM based
technique and found that for some combined HMM-
SVM technique is better than HMM. Combined
HMM-SVM classifier improve the problem of HMM
classifier of multiple detection of Class.
After describing cloud computing in Section
3 Handwritten character recognition techniques is
discussed in Section 4, Segmentation is discussed in
section 5, and Feature Extraction types discussed in
section 6, Character Classification is discussed in
Section 7. Proposed system of Cloud based
Handwritten Character Recognition is discussed in Section 8.Finally, Discussion on review is discussed
in Section 9.
3. Cloud Computing
Cloud computing is the junction of several
concepts from resource pooling, virtualization,
dynamic provisioning, and utility computing.
Applications are easily deployed where the
underlying technology components can expand and
contract with the flow of the business cycle. Cloud
computing is a new method to add capability to a
computer without licensing new software, investing
in new hardware or infrastructure or training new personnel. Applications are purchased and run over
the network listed users’ desktop. A cloud server is a
logical server that is built, hosted and delivered
through a cloud computing platform over the
Internet. Cloud servers possess and exhibit similar
capabilities and functionality to a typical server but
are accessed remotely from a cloud service provider.
Most of the current clouds are built on top of modern
data centers. It incorporates Infrastructure as a
Service (IaaS), Platform as a Service (PaaS), and
Software as a Service (SaaS), and provides these
services like utilities, so the end users are billed by
how much they used[13]. IaaS provides service such
as memory, CPU and storage. It reduces hardware
cost. That means customer use a virtualized server
and running software on it. Amazon EC2, is the best
example of IaaS for storage and maintaining virtual servers.
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4. Handwritten Character Recognition
Techniques The general steps of handwriting recognition
include preprocessing, segmentation, feature extraction, classification and so on. The
preprocessing technology consists of binarization,
normalization, and smoothing and slant correction.
The widely used feature extraction methods for
handwritten Marathi character recognition include 8-
directional feature extraction and Gradient feature
extraction etc. In classification step, Support Vector
Machine (SVM) classifier is mainly used.
5. Segmentation
Segmentation is the most important part of
the pre-processing stage. It allows the recognizer to
extract features from each individual character. In the
more complicated case of handwritten text, the
segmentation problem becomes much more difficult as
quality have a tendency to be connected to each other,
overlapped or unclear. Segmentation is done to break
the single text line, single word and single character
from the input document. For isolated characters or
numerals, segmentation task is not that difficult.
However, for joint and complex strings more advanced
techniques required to be employed. There are two
types of segmentations: 1. External segmentation isolates various writing units such as paragraphs,
sentences or words. 2. Internal segmentation, which is
isolation of letters. Character segmentation strategies
are divided into three techniques: Explicit
Segmentation-This approach is used to identify the
smallest possible word segments that may be smaller
than letters, but surely cannot be segmented further. It
is done by the process of analysis. Implicit
Segmentation- This is based on recognition. It
searches the image for components that match
predefined classes. Mixed Strategies- They combine
explicit and implicit segmentation in a hybrid way[14].
6. Feature Extraction Techniques
After the segmentation procedure the feature
set is extracted. Extracted features from the character
images are used to train the Support vector Machine.
In this stage, the features of the characters that are
used for classifying them at recognition stage are
extracted. This is an important stage as its efficient
performance improves the recognition rate and
reduces the misclassification. A good feature set
should represent characteristic of a class that help
distinguish it from other classes. The widely used
feature extraction methods are Template matching,
Deformable templates, Unitary Image transforms,
Graph description, Projection Histograms, Contour
profiles, Zoning, Geometric moment invariants,
Zernike Moments, Spline curve approximation,
Fourier descriptors, Gabor feature. Due to the nature
of handwriting with its high degree of inconsistency
and ambiguity extracting these features, is a difficult
task. Chain code Histogram of Character contour and
gradient feature extraction techniques are used in this paper[15].
6.1 Chain Code Histogram of Character
Contour
Given a scaled binary image, first find the
contour points of the character image. Consider a 3 × 3 window surrounded by the object points of the
image. If any of the 4-connected neighbor points is a
background point then the object point (P), as shown
in Figure 1 is considered as contour point.
Figure 1. Contour point detection
The contour following procedure uses a contour
representation called “chain coding” that is used for
contour shown in Figure 1.4. Each pixel of the
contour is assigned a different code that indicates the
direction of the next pixel that belongs to the contour
in some given direction. Chain code provides the points in relative position to one another, independent
of the coordinate system. In this methodology of
using a chain coding of connecting neighboring
contour pixels, the points and the outline coding are
captured. Contour following procedure may proceed
in clockwise or in counter clockwise direction. Here,
we have chosen to proceed in a clockwise direction
[16].
(a) (b) (c)
Figure 2. Chain Coding: (a) direction of
connectivity, (b) 4-connectivity, (c) 8-
connectivity. Generate the chain code by
detecting the direction of the next-in-line
pixel
6.2 Gradient Feature Extraction
The gradient measures the magnitude and
direction of the greatest change in intensity in a small
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neighborhood of each pixel. (In what follows,
"gradient" refers to both the gradient magnitude and
direction). Gradients are computed by means of the
Sobel operator. The Sobel templates used to compute
the horizontal (X) & vertical (Y) components of the
gradient are shown in Figure 3. The arc tangent of
the Gradient and Gaussian filter. A modified
quadratic classifier is applied on the features of
handwritten characters for recognition. Elastic matching (EM) technique based on an Eigen
deformation (ED) for recognition of handwritten
Devnagari characters is proposed in [17].
Figure 3. Contour point detection
Horizontal Component Vertical Component
Figure 3 Sobel masks for gradient Given an input
image of size D1×D2, each pixel neighborhood is
convolved with these templates to determine these X and Y components, Sx and Sy, respectively. Eq. (1)
and (2) represents their mathematical representation:
(1) (2) Here, (i,j) range over the image rows (D1)
and columns (D2), respectively. The gradient
strength and direction can be computed from the
gradient vector [Sx, Sy] T as shown below using Eq.
(3) and (4) Sx i, j = I i − 1, j + 1 + 2 ∗ I i, j + 1 + I i + 1, j + 1 − i − 1, j − 1 − 2 ∗ I i, j − 1 − I i + 1, j − 1 (1)
𝑆𝑦 𝑖, 𝑗 = 𝐼 𝑖 − 1, 𝑗 − 1 + 2 ∗ 𝐼 𝑖 − 1, 𝑗 + 𝐼 𝑖 − 1, 𝑗 + 1 − 𝐼 𝑖 + 1, 𝑗 − 1 − 2 ∗ 𝐼(𝑖 + 1, 𝑗) − 𝐼 𝑖 + 1, 𝑗 + 1
(2)
The gradient magnitude is then calculated as:
𝑟 𝑖, 𝑗 = Sx2 i, j + Sy2 i, j (3)
Gradient direction is calculated as:
𝜃 𝑖, 𝑗 = 𝑡𝑎𝑛^(−1) 𝑆𝑦 𝑖 ,𝑗
𝑆𝑥 𝑖 ,𝑗 (4)
cos ∝ +𝑐𝑜𝑠𝛽
= 2 cos 1
2 𝛼 + 𝛽 cos[
1
2 𝛼 − 𝛽 ]
After obtaining gradient vector of each
pixel, the gradient image is decomposed into four
orientation planes or eight direction planes (chain
code directions) as shown in Figure 1.4.
Figure 4. Contour point detection of gradient vector
After this, gradient vector of each pixel is
decomposed into components along these standard
direction planes. If a gradient direction lies between
two standard directions, it is decomposed into two
components in the two standard directions.
7. Classifiers
The decision making part of a recognition
system is the classification stage and it uses the
features extracted from the previous stage. There are
various methods for classification. K Nearest
Neighbor (KNN), Artificial Neural Network(ANN)
and Support Vector Machine (SVM) In this paper,
we discuss the characteristics of the some
classification methods that have been successfully applied to Offline Devnagari Marathi character
recognition and results of SVM classification is better
than other classification methods, applied on
Handwritten Devnagari characters.
7.1 Support Vector Machine Classifier
Support Vector Machine is relatively new
classification technique producing efficient
recognition results. SVM is a new type of hyperplane
classifier developed based on statistical learning
theory given by Vapnik. Basically SVM is a binary
(two class) linear classifier in kernel induced feature
space and is formulated as a weighted combination of
kernel functions on training examples. The kernel
function represents the inner product of two vectors
in linear/nonlinear feature space. Multiclass
classification is accomplished by combining multiple
binary SVM classifiers. Support Vector Machine is
supervised learning tool which is used for
classification and regression. The basic SVM takes a
set of input data and predicts, for each given input. A
set of training examples given each marked as
belonging to one of two categories, a SVM training algorithm builds a model that assigns new examples
into one category or the other. A support vector
machine constructs a hyper plane or set of hyper
planes in a high dimensional space, which can be
used for classification, regression or other tasks.
Naturally, a good separation is achieved by the hyper
plane that has the largest distance to the nearest
training data point of any class (so-called functional
margin), since in general the larger the margin the
lower the generalization error of the classifier[18].
Figure 5. Feature transformation using SVM
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There are two types of SVM:1)Linear SVM
& 2) Non Linear SVM. Linear SVM is the newest
extremely fast machine learning (data mining)
algorithm for solving multiclass classification
problems from ultra large data sets that implements
an original proprietary version of a cutting plane
algorithm for designing a linear support vector
machine. Linear SVM is a linearly scalable routine
meaning that it creates an SVM model in a CPU time which scales linearly with the size of the training data
set. SVM models clearly show its superior
performance when high accuracy is required.
Features: 1. Efficiency in dealing with extra large data sets
(say, several millions training data pairs),
2. Solution of multiclass classification problems
with any number of classes,
3. Working with high dimensional data (thousands
of features, attributes) in both sparse and dense
format,
4. No need for expensive computing resources
(personal computer is a standard platform),
5. Ideal for contemporary applications in digital
advertisement, e-commerce, web page
categorization, text classification, bioinformatics,
proteomics, banking services and many other areas.
Linear functions can control the complexity of
a learning machine. To avoided the problem of
dealing with too complex functions at the value of
being able to solve only linearly separable problems.
Following diagram will show how to extend the
linear SVM for constructing a very rich set of non-
linear decision functions while at the same time
controlling their complexity.
Figure 6. Three different views on the same two class separation problem.(a) A linear
separation of the input points in not possible without errors.(b) A better separation is
provided by a non linear surface in the input space. (c) This non linear surface
corresponds to a linear surface in a feature space
7.2 Kernels of SVM Classifiers
In addition to basic linear kernel, many other
kernels are also proposed by researchers. Following
are the four basic kernels used in SVM
classifications:
Linear: 𝐾 𝑥𝑖, 𝑥𝑗 = 𝑥𝑖𝑇. 𝑥𝑗 Polynomial : 𝐾 𝑥𝑖, 𝑥𝑗 = (𝛾𝑥𝑖𝑇 .𝑥𝑗 + 𝑟)𝑑 . 𝛾 > 0. Radial Basis Function(RBF):
K xi, xj = exp −γ xi − xj 2 , γ > 0.
Sigmoid: K xi. xj = tanh γxiTxj + r . Here and are kernel parameters. Among these kernels
RBF kernel is mostly used[19].
Figure 7. Linear separating hyper planes for the separable case. The support vectors are
circled
𝐷 𝑥 = 𝑊 𝑡 𝑥 +b (1) where w is an m-dimensional vector, b is a scalar,
𝑦𝑖𝐷 𝑋𝑖 ≥ 1 − ξi𝑓𝑜𝑟 𝑖 = 1, … . , 𝑀 (2)
Here ξi are nonnegative slack variables. The distance
between the separating hyper plane D(x) = 0 and the training datum, with ξi = 0, nearest to the hyper plane
is called margin. The hyper plane D(x) = 0 with the
maximum margin is called optimal separating hyper
plane. To determine the optimal
separating hyper plane, we minimize
1/2| 𝑊 |2 +C ξi 𝑀𝑖−1 (3)
subject to the constraints:
𝑦𝑖 𝑊𝑡𝑋𝑖 + 𝑏 ≥ 1 − ξi for i = 1,… . , M (4) where, C is the margin parameter that determines the
tradeoff between the maximization of the margin and
minimization of the classification error. The data that
satisfy the equality in (4) are called support vectors.
[20].
8. Proposed System
8.1 Challenges solution of System 1) How to identify variability for same character?
The main challenges in offline handwritten
character for Indian languages is to build a system
that is able to distinguish between variation in
writing the same stroke and minor variation in
similar characters in the script. The recognition
system should be able to distinguish between
structural or shape variations across similar
characters and the natural variability that exist
when the same character is written by different
persons or same character written at different
times. The feature extraction and stroke
classification address this issue to identify an
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appropriate representation and method of
classification.
2) Which dataset can be used for handwritten
recognition?
The standard database for Indian script is neither
available freely nor commercially, hence we have
collected the samples Devnagari handwritten
characters data from different professional
belonging to schools, colleges, nor commercial sectors are collected and created the data set.
Generation of database from the scanned
datasheets.
3) Which techniques are used to extract the features
for recognition of characters?
Feature extraction is extracting information from
raw data which is most relevant for classification
purpose and that minimizes the variations within a
class and maximizes the variations between
classes. Different feature extraction methods are
designed for different representations of the
characters, such as solid binary characters,
character contours, skeletons or gray level sub
images of each individual character. In above
discussion we have studied that there are so many
features extraction techniques. In that we have
used chain code histogram to character contour and gradient features for recognition of Marathi
characters.
4) Which classifiers are used?
SVM is better than other classifier because of its
complexity of training, flexibility, classification
accuracy and complexity which is given bellow.
8.2 Cloud Based Recognition System
The architecture of the proposed Cloud-
based handwriting recognition system is shown in
Figure 8. The client terminal is Computer device or
laptop with different operating systems, such as
Windows XP, Windows 7 Embedded Linux so on.
There is a scanned image of the handwritten
character through which we take the strokes of input
character. The server is virtual Cloud combining
many physical server machines and personal
computers. When finishing writing, strokes are sent
to the server by socket protocol through WiFi,
GPRS, EDGE or the 3G networks. The handwriting recognition are run on the Cloud server Relative to
the bandwidths of the networks, the data of the
strokes and candidates is small, so it costs very little
transmission time and users have no sense about the
delay. It feels like that the recognition is done locally
In theory, the memory and computing capacity of
Cloud server are infinite. So, we can implement any
state-of-the art recognition algorithm to get very high
accuracy in real time[2].
Figure 8. Architecture of cloud based system
9. Discussion on Review
9.1 Preprocessing
Pre-processing aims to produce data that are
easy for the character recognition systems to operate
accurately. The main objectives of pre-processing
are:
1) Binarization: If the illumination over the
document is not uniform, for instance in the case of
scanned book pages or camera-captured documents,
global binarization methods tend to produce
marginal noise along the page borders. To overcome
these complexities, local thresholding
techniqueshave been proposed for document
binarization[21].
2) Normalization: Normalization is used to reduce the variation of
shapes of the character. Size normalization depends
on how user moves the pen on writing pad. Centering
is required when pen is moved along the border of
writing pad. Normalization is for reducing variability
in character size and pen velocity
3) Smoothing: Smoothing operations are used to blur
the image and reduce the noise. Smoothing and noise
removal can be done by filtering.
4) Interpolation: Input handwritten characters could
have missing points when handwriting speed is fast.
It is used to find missing points in characters.
Bresenham’s line algorithms can be used to estimate
the missing intermediate points.
5) Slant Correction: Slant correction and normalizing
slant is required to correct the shape of input
handwritten character which is bending in left or
right directions. For this slant correction DESLANT algorithm is used. Slant correction method uses the
vertical projection histogram. The idea is that the
histogram of a word that is written straight up will
have larger and more distinct peaks. Therefore, look
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at the histogram of the word at different shear angles
and take the one with the highest peaks. For angles
between −45 and 45 degrees, which is the most
common range of slant angles in normal writing[22].
9.2 Segmentation
In complicated case of handwritten text, the
segmentation problem becomes difficult when letters
tend to be connected to each other, overlapped or
distorted. Segmentation is break the single text line,
single word and single character from the input
document. For isolated characters or numerals,
segmentation task is not that difficult. However, for
joint and complex strings more advanced techniques
required to be employed. Average Longest path algorithm is used in different handwriting styles
large variation of neighboring characters within
words are usually connected for that time need to
segment the word into individual character for
accurate character recognition[23].
9.3 Feature Extraction Techniques
There are various feature extraction
techniques but freeman chain code and gradient
feature extraction techniques are better. The freeman
chain code approach is robustness to small variation
and easy to implement and which provides good
recognition rate. The gradient feature extraction
technique can be easily used to gray scale images
and are robust against image noise and edge
direction variation.
9.4 Classifier
Neural classifiers, K Nearest Neighbor and
SVMs show different properties in the following
respects[24][25].
Complexity of training: The parameters of neural classifiers are generally adjusted by gradient descent.
By feeding the training samples a fixed number of
sweeps, the training time is linear with the number of
samples. K-NN classifiers are generally adjusted by
distance measure. By feeding the training samples a
fixed number of sweeps, the training time is linear
with the number of samples. SVMs are trained by
quadratic programming (QP), and the training time is
generally proportional to the square of number of
samples. Some fast SVM training algorithms with
nearly linear complexity are available, however.
Flexibility of training: The parameters of neural
classifiers can be adjusted in string-level or layout-
level training by gradient descent with the aim of
optimizing the global performance. In this case, the
neural classifier is embedded in the string or layout
recognizer for character recognition. The parameters of K-NN classifiers can be adjusted in Feature
weighting improve classification accuracy for global
performance and also easy to add a new class to an
existing classifier. On the other hand, SVMs can only
be trained at the level of holistic patterns.
Classification accuracy: SVMs have been
demonstrated superior classification accuracies to
neural classifiers in many experiments. The
parameters of K-NN classifiers can be adjusted in
Feature weighting improve classification accuracy for
global performance and also easy to add a new class to an existing classifier.
Storage and execution complexity: SVM learning
by QP often results in a large number of SVs, which
should be stored and computed in classification.
Neural classifiers have much less parameters, and the
number of parameters is easy to control. In a word,
neural classifiers consume less storage and
computation than SVMs. K-NN classifiers have much
less parameters, and are easy to control. In a word, K-
NN classifiers consume less storage and computation
than SVMs.
In above discussion SVM is better than
KNN and ANN classifier because of its complexity
of training, flexibility, classification accuracy and
complexity. The SVM is a machine learning
algorithm which
Solves classification problems
Uses a flexible representation of the class
boundaries
Implements automatic complexity control to reduce
over fitting
Has a single global minimum which can be found
in polynomial time.
SVM is popular because
It can be easy to use
It often has good generalization performance
The same algorithm solves a variety of problems
with little tuning.
Conclusion The idea of regarding handwritten character
recognition as a service based on Cloud computing, it
can be more convenient. These clouds can evolve as
new data is received by the server, improving
recognition. These clouds also provide a simple but
effective method for handwriting recognition. Among
all the methods it is found that chain code histogram
of a character contour and gradient feature extraction
techniques are the best performing with SVM
classifier.
References [1] Vadim Mazalov and Stephen M. Watt,“ Writing on
Clouds”
[2]Yan Gao, Lanwen Jin+, Cong He, Guibin Zhou,”
Handwriting Character Recognition as a Service:A
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ISSN:2249-5789
New Handwriting Recognition System Based on Cloud
Computing”, ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and
Recognition, pp. 885-889, 2011 IEEE.
[3]K. Sethi and B. Chatterjee, “Machine Recognition
constrained Hand printed Devanagari”, PatternRecognition, Vol. 9, pp. 9-75, 1977.
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