MAIN PPT2
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Transcript of MAIN PPT2
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UNICODE OCR
N. Manasa (07T81A1220)A.Sruthi (07T81A1249)
B.Venkatalaxmi (07T81A1256)
N.Shanthi kishore (07T81A1239)
By
Under the Guidance of
R.Ravinder reddy
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Abstract
IntroductionSystem Analysis
System Requirements
System Design
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Abstract
Character degradation is a big problem for machine printed character recognition.
Two main reasons for degradation are extrinsic image degradation such as blurring
and low image dimension, and intrinsic degradation caused by font variations. A
recognition method that combines two complementary classifiers is proposed in
this paper.
The local feature based classifier extracts the local contour direction changes,which is effective for character patterns with less structure deterioration.
The global feature based classifier extracts the texture distribution of the character
image, which is effective when the character structure is hard to discriminate. The
two complementary classifiers are combined by candidate fusion in a coarse-to-fine
style. Experiments are carried on degraded Chinese character recognition.
The results prove the effectiveness of our method.
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Introduction
The process of converting scanned images of machine-printed or handwritten text
(numerals, letters, and symbols) into a computer-processable format; also known as
optical character recognition (OCR).
A typical OCR system contains three logical components: an image scanner,
OCR software and hardware, and an output interface.The image scanner optically captures text images to be recognized. Text images
are processed with OCR software and hardware.
The process involves three operations: document analysis (extracting individual
character images), recognizing these images (based on shape), and contextual
processing (either to correct misclassifications made by the recognition algorithm
or to limit recognition choices).
The output interface is responsible for communication of OCR system results to
the outside world.
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OCR is the mechanical or electronic translation of scanned images of handwritten,
typewritten or printed text into machine-encoded text.
There are many different approaches to optical character recognition problem. One
of the most common and popular approaches is based on neural networks, which
can be applied to different tasks, such as pattern recognition, time series prediction,
function approximation, clustering, etc.
As more and more convenient document capture devices emerge in the market, the
demands for degraded character recognition increase dramatically. Many research
results are published in recent years for degraded character recognition.
The solution to the intrinsic shape degradation can be well solved by the
nonlinear normalization and the block based local feature extraction used in
handprint character recognition.
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As for the extrinsic image degradation, a comprehensive study on
the image degradation model on Latin character set is presented in.
There are basically two approaches for the extrinsic degradation:
the local based grayscale feature extraction and the global based
texture feature extraction . While most of the methods focus onhow to solve one of the above problems, few papers robust against
the combination of the both.
The recognition confidence and estimated image blurring level areused to combine a local feature based classifier with a global
feature based classifier. However, the hierarchical recognition
structure cannot efficiently handle the mixture cases in real
environment.
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System Analysis:
oExisting System
oDisadvantage
oProposed System
oAdvantage
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Existing System
In the existing system the Character degradation is a big problemfor machine printed character recognition.
Two main reasons for degradation are the intrinsic degradation
caused by character shape variation and the extrinsic imagedegradation such as blurring and low image dimension.
A mixture of the above factors makes degraded character
recognition a difficult task.
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Proposed System
In the proposed system intrinsic problem and extrinsic
problems can be solved by the complementary classifiermethod which consisting of local and global features
two classification processes are executed in parallel under a
coarse-to fine recognition structure.
In the proposed system the Unicode OCR method uses hybrid
recognition algorithm is proposed to solve the problem in the
existing system.
It is used to find the characters fonts, its size, its width and its
height.
It mainly employees an approach called Neural Networks.
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Neural networks
Neural Networks usually called Artificial Neural Networks.
It is a mathematical model or computational model that isinspired by the structure or functional aspects of biological
neural networks.
A neural network consists of an interconnected group of
artificial neurons, and it processes information using aconnectionist approach to computation.
An artificial neuron receives a number of inputs either from
original data, or from the output of other neurons in the neural
network. Each input comes via a connection that has a strength or
weight. These weights correspond to synaptic efficiency in a
biological neuron.
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In this it consists of 3 phases which are Training set, Loading, and Recognition
1. Training set: In the training step we teach the network to respond with desiredoutput for a specified input. For this purpose each training sample is
represented by two components: possible input and the desired network's
output for the input.
2. Loading : After the training step is done, we can give an arbitrary input to the
network and the network will form an output, from which we can resolve a
pattern type presented to the network.
3. Recognition: Recognition will be done only if errors will be less i.e nothing
but On each learning epoch all samples from the training set are presented to
the network and the summary squared error is calculated. When the error
becomes less than the specified error limit, then the training is done and the
network can be used for recognition. 92 percent accuracy in recognition proves
the good performance of the proposed system.
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Proposed System(cont..) How to recognize ?
We need to input to the trained network and get itsoutput. Then we should find an element in the output vectorwith the maximum value. The element's number will point usto the recognized pattern:
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Advantage
In our system classifiers combination was implemented
using fusion to solve the problem of degradation.
The result shows that the proposed method is much more
robust than any of the individual classifier
Normalization technique was used which provide exact
pixel identification and high speed in character recognition .
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REQUIREMENTS
DESIGNS
SCREEN SHOTS
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DFD DIGRAMS
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Source image Processing usingartificial neuralnetwork
Characterreorganization(paragraphs, lines,words, characters)
Unicode mapping ClassificationFeature extraction (characterheight, width, horz lines,vertical lines, slope lines)
Recognized text
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Use-case diagram
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Class diagram
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SEQUENCE DIAGRAM
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Screen shots
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Load train set
i k
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Saving network
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Loading network and the image
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Future Enhancements
The future research directions include better coarse
classification and the robustness evaluation under other
degradation types.
Experiments are carried on degraded Chinese characterrecognition.
Even we can implement our system to detect the degraded
characters of other languages and to convert them into required
language.
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THANK YOU