Adaptive Membership
Functions for Hand-Written Character Recognition by Voronoi-based Image Zoning
ABSTRACT:
In the field of hand-written character
recognition, image zoning is a widespread technique for feature extraction since
it is rightly considered able to cope with hand-written pattern variability. As
a matter of fact, the problem of zoning design
has attracted many researchers that have
proposed several image zoning topologies, according to static and dynamic strategies.
Unfortunately, little attention has been paid so far to the role of
feature-zone membership functions, that define the way in which a feature
influences different zones of the zoning method. The results is that the
membership functions defined to date follow non-adaptive, global approaches
that are unable to model local information on feature distributions. In this
paper, a new class of zone-based membership functions with adaptive
capabilities is introduced and its effectiveness is shown. The basic idea is to
select, for each zone of the zoning method, the membership function best suited
to exploit the characteristics of the feature distribution of that zone. In
addition, a genetic algorithm is proposed to determine – in a unique process -
the most favorable membership functions along with the optimal zoning topology,
described by Voronoi tessellation. The experimental tests show the superiority
of the new technique with respect to traditional zoning methods.
EXISTING
SYSTEM:
In existing system, the problem of
zoning design has been mainly considered as related to the design of the
topology to be used, that defines the way in which a pattern image must be segmented
in order to extract as much discriminative information as possible.
The approaches proposed so far for
topology design can be divided into two categories: static and dynamic
Traditional approaches involve static topologies
that are designed without using a-priori information on feature distributions in
pattern classes. In this case, zoning design is performed according to
experimental evidences or on the basis of intuition and experience of the
designer.
In general, static topologies are
designed considering u×v regular grids that are superimposed on the pattern
image, determining uniform partitions of the pattern image into regions of
equal shape.
DISADVANTAGES
OF EXISTING SYSTEM:
The problem of zoning design has been
mainly considered as related to the design of the topology to be used, that
defines the way in which a pattern image must be segmented in order to extract
as much discriminative information as possible.
The results is that the membership
functions defined to date follow non-adaptive, global approaches that are
unable to model local information on feature distributions.
PROPOSED
SYSTEM:
The proposed system introduces a new class
of zone-based membership functions with adaptive capabilities and presents a
real-coded genetic algorithm for determining – in a single process - both the
optimal zoning method, based on Voronoi tessellation of the pattern image, and
the adaptive membership function most profitable for a given classification
problem.
Contrary to other approaches proposed in
literature so far, the new class of membership
functions allows the membership function to adapt to the specific
feature distribution of each zone of the zoning method.
ADVANTAGES
OF PROPOSED SYSTEM:
·
The experimental tests show the
superiority of the new technique with respect to traditional zoning methods.
·
The proposed system is capable of intelligible
handwritten input from sources such as paper documents, photographs,
touch-screens and other devices.
·
Can
be used effectively in many applications like:
o
Signature
Verification
o
Postal-Address
Interpretation
o
Bank-Check
Processing
MODULES:
1. Creating the Character Recognition System
2. Training
3.
Abstract-level
membership functions
4. Testing
Phase
MODULES
DESCRIPTION:
Creating the Character Recognition System
The Character Recognition System must first be created through a
few simple steps in order to prepare it for presentation into java. The
matrixes of each letter of the alphabet must be created along with the network
structure. In addition, one must understand how to pull the Binary Input Code
from the matrix, and how to interpret the Binary Output Code, which the
computer ultimately produces.
First, in order to endow a computer with the
ability to recognize characters, we must first create those characters. The
first thing to think about when creating a matrix is the size that will be
used. Too small and all the letters may not be able to be created, especially
if you want to use two different fonts. On the other hand, if the size of the
matrix is very big, there may be a few problems: Despite the fact that the
speed of computers doubles every third year, there may not be enough processing
power currently available to run in real time. Training may take days, and
results may take hours. In addition, the computer’s memory may not be able to
handle enough neurons in the hidden layer needed to efficient and accurately
process the information. A large matrix size of 20 x 20 was created, through
the steps as explained above, because it may not be able to process in real
time. (See Figure 1)
Training
To create a network that can handle noisy input
vectors it is best to train the network on both ideal and noisy vectors. To do
this, the network is first trained on ideal vectors until it has a low sum
squared error. Then, the network is trained on all sets of ideal and noisy
vectors. The network is trained on two copies of the noise-free alphabet at the
same time as it is trained on noisy vectors. The two copies of the noise-free
alphabet are used to maintain the network's ability to classify ideal input vectors.
Unfortunately, after the training described above the network may have learned
to classify some difficult noisy vectors at the expense of properly classifying
a noise-free vector. Therefore, the network is again trained on just ideal
vectors. This ensures that the network responds perfectly when presented with
an ideal letter.
Training Phase
Training
Phase has various functionalities such as:
ü Analyze
image for characters
ü Convert
symbols to pixel matrices
ü Retrieve
corresponding desired output character and convert to Unicode
ü Lineraize
matrix and feed to network
ü Compute
output
ü Compare
output with desired output Unicode value and compute error
ü Adjust
weights accordingly and repeat process until preset number of iterations
Abstract-level
membership functions
Membership functions at abstract-level
assign Boolean influence weights on the basis of the first k zones in RISi (Ranked Index Sequence): This is
the standard membership function used in traditional zoning-based
classification.
A character matrix is an array of black and
white pixels; the vector of 1 represented by black, and 0 by white. They are
created manually by the user, in whatever size or font imaginable; in addition,
multiple fonts of the same alphabet may even be used under separate training
sessions.
Testing Phase
Testing
Phase has various functionalities such as:
ü Analyze
image for characters
ü Convert
symbols to pixel matrices
ü Compute
output
ü Display
character representation of the Unicode output
The process of image analysis to
detect character symbols by examining pixels is the core part of input set
preparation in both the training and testing phase. Symbolic extents are
recognized out of an input image file based on the color value of individual
pixels, which for the limits of this project is assumed to be either black RGB(255,0,0,0)
or white RGB(255,255,255,255). The input images are assumed to be in
bitmap form of any resolution which can be mapped to an internal bitmap object
in the Microsoft Visual Studio environment. The procedure also assumes the
input image is composed of only characters and any other type of bounding
object like a border line is not taken into consideration.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
PROCESSOR :
PENTIUM IV 2.6 GHz
RAM
: 512 MB
MONITOR : 15”
HARD
DISK : 20 GB
CDDRIVE : 52X
KEYBOARD : STANDARD
102 KEYS
MOUSE
: 3 BUTTONS
SOFTWARE
REQUIREMENTS:
FRONT
END : JAVA (APPLET)
TOOLS
USED : JFRAME BUILDER
OPERATING
SYSTEM: WINDOWS XP
REFERENCE:
G. Pirlo, Member, IEEE, D. Impedovo, Member,
IEEE, “Adaptive Membership Functions for Hand-Written Character Recognition
by Voronoi-based Image Zoning”, IEEE
TRANSACTIONS ON IMAGE PROCESSING, 2012.
