The document proposes a new adaptive membership function approach for handwritten character recognition using image zoning. Existing zoning methods use static, non-adaptive membership functions that cannot model local feature distributions. The proposed system introduces adaptive membership functions selected for each zone using a genetic algorithm. It determines the optimal zoning topology and adaptive membership functions in a single process. Experimental results show it performs better than traditional zoning methods.

1. 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.

2. 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.

3. 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.

4. 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:

5. 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)

6. Training

7. 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:
 Analyze image for characters
 Convert symbols to pixel matrices
 Retrieve corresponding desired output character and convert to Unicode
 Lineraize matrix and feed to network
 Compute output

8.  Compare output with desired output Unicode value and compute error
 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:
 Analyze image for characters
 Convert symbols to pixel matrices

9.  Compute output
 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”

10. 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.