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Project Reporton

Pattern Recognition Using Neural network with Matlabs (Character Recognition)Lovely Faculty of Technology & Sciences Department of ECE/EEE/D6 DomainAmlani Pratik Ketanbhai Reg. No. 10903436 SEC: E38E1Roll No. RE38E1A06

Lovely professional University Jalandhar- Delhi Highway, GT Road NH-1, Phagwara -144402

CERTIFICATE

This is to certify that AMLANI PRATIK KETANBHAI bearing Registration no. 10903436 has completed dissertation/capstone project titled, PATTERN RECOGNITION USING NEURAL NETWORK WITH MATLABs under my guidance and supervision. To the best of my knowledge, the present work is the result of her original investigation and study. No part of the dissertation has ever been submitted for any other degree at any University. The dissertation is fit for submission and the partial fulfillment of the conditions for the award of Capstone project.

Mr. Srinivas Perala UID 14910 Assistant professor of ECE/EEE Lovely Professional University Phagwara, Punjab. Date :

DECLARATION

I AMLANI PRATIK KETANBHAI student of Lovely faculty of Technology & Sciences , B.Tech ECE LEET RE38E1 under Department of Electronics & Communications Engineering of Lovely Professional University, Punjab, hereby declare that all the information furnished in this dissertation / capstone project report is based on my own intensive research and is genuine. This dissertation / report does not, to the best of my knowledge, contain part of my work which has been submitted for the award of my degree either of this university or any other university without proper citation.

Date : 26/11/2011

AMLANI PRATIK KETANBHAI Reg. No. 10903436 SEC: E38E1Roll No. RE38E1A06

PATTERN RECOGNITION USING NEURAL NETWORK WITH MATLABs

2011

INDEXTopic No 1.0 1.1 1.2 2.0 2.1 2.2 2.3 3.0 3.1 3.2 3.3 4.0 ABSTRACT INTRODUCTION KEYWORDS WHAT IS NEURAL NETWORK? APPLICATIONS OF NEURAL NETWORKS BIOLOGICAL INSPIRATION GATHERING DATA FOR NEURAL NETWORK BASIC ARTIFICIAL MODEL PATTER RECOGNITION TRAINING MULTILAYER PERCEPTRON BACKPROPAGATION ALGORITHM HANDWRITTEN ENGLISH ALPHABET RECOGNITION SYSTEM HANDWRITTEN CHARACTER RECOGNITION SYSTEM TRAINING METHOD WITH REJECT OUTPUT CHARACTER RECOGNITION BY LEARNING RULE ARTIFICIAL NEURAL NETWORK TRAINING MATLAB IMPLEMENTATION TRANSLATING THE INPUT INTO MATLAB CODE CHARACTER RECOGNITION MATLABs SOURCE CODE REFRENCE S Topic Name Page No. 5 5 6 6 7 7 8 8 11 13 14 15

4.1 4.2 5.0 5.1 5.2 5.3 6.0 6.1

18 21 23 26 27 27 31 49

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1.0 AbstractAbstractIn this paper, I have tried develop a neural network that can be used for handwritten character recognition. Supervised learning is used in order to provide training to the neural network. The neural network is designed here with a reject output so that we can easily classify all the training patterns exactly. Competitive learning algorithm is used in order to get the best result. The fine-classifiers are realized using multilayered neural networks, each of which solves the two-category classification problem. We also use the rough-classifier for the selection the training samples in the learning process of multilayered neural networks in order to reduce the learning time.

1.1 IntroductionOne of the most important effects the field of Cognitive Science can have on the field of Computer Science is the development of technologies that make our tools more human. A very relevant present-day field of natural interface research is hand writing recognition technology. Evidenced by the fact that we are not currently all using Tablet computers, accurate hand writing recognition is clearly a difficult problem to solve. Before pattern recognition, air photo interpretation and photo reading from aerial reconnaissance were used to define objects and its significance. In this process, the human interpreter must have vast experience and imagination to perform the task efficiently and the interpreters judgment is considered subjective. Just recently, applications utilizing aerial photographs have multiplied and significant technological advancements have emerged from plain aerial photographs into high-resolution digital images. Pattern recognition is now widely used in large scale application such as monitoring the changes in water levels of lakes and reservoirs, assessing crop diseases, assessing land-use and mapping archaeological sites Each character is written with a single stroke. This solves the character level segmentation problem that previously plagued handwriting recognition. The curve drawn between pen down and pen up events can be recognized in isolation. Uni-stroke recognition algorithms can be relatively simple because there is no need to decide which parts of the curve belong to which character or to wait for more strokes that belong to the same character as is often the case when we try to recognize conventional handwriting. To develop a handwriting recognition system that is both as reliable as Uni-stroke, and natural enough to be comfortable, the system must be highly adaptable.

1.2Key wordsPattern recognition, Hidden Markov Model, Learning rules in Neural N/W, Matlab Toolbox.

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2.0 What is Neural Network?Neural Networks are a different paradigm for computing:

Von Neumann machines are based on the processing/memory abstraction of human information processing. Neural networks are based on the parallel architecture of animal brains.

Neural networks are a form of multiprocessor computer system, with

simple processing elements a high degree of interconnection simple scalar messages adaptive interaction between elements

A biological neuron may have as many as 10,000 different inputs, and may send its output (the nth presence or absence of a short-duration spike) to many other neurons. Neurons are wired up in a 3-dimensional pattern.

Real brains, however, are orders of magnitude more complex than any artificial neural network so far considered.

2.1 Application of Neural NetworkNeural networks are applicable in virtually every situation in which a relationship between the predictor variables (independents inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of "correlations" or "differences between groups." A few representative examples of problems to which neural network analysis has been applied successfully are: Detection of medical phenomena Stock market prediction Credit assignment Monitoring the condition of machinery Engine management

2.2 Biological Inspiration

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Neural networks grew out of research in Artificial Intelligence; specifically, attempts to mimic the fault-tolerance and capacity to learn of biological neural systems by modeling the low-level structure of the brain (see Patterson, 1996). The main branch of Artificial Intelligence research in the 1960s -1980s produced Expert Systems. These are based upon a high-level model of reasoning processes (specifically, the concept that our reasoning processes are built upon manipulation of symbols). It became rapidly apparent that these systems, although very useful in some domains, failed to capture certain key aspects of human intelligence. According to one line of speculation, this was due to their failure to mimic the underlying structure of the brain. In order to reproduce intelligence, it would be necessary to build systems with a similar architecture.

Basic artificial Neuron model developed by various transfer function.

The brain is principally composed of a very large number (circa 10,000,000,000) of neurons, massively interconnected (with an average of several thousand interconnects per neuron, although this varies enormously). Each neuron is a specialized cell which can propagate an electrochemical signal.

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Human Neuron Model consisting with millions of element to form a brain and transfers the signal.

In general, if you use a neural network, you won't know the exact nature of the relationship between inputs and outputs - if you knew the relationship, you would model it directly. The other key feature of neural networks is that they learn the input/output relationship through training. There are two types of training used in neural networks, with different types of networks using different types of training. These are supervised and unsupervised training, of which supervised is the most common and will be discussed in this section.

2.3 Gathering Data for Neural NetworksOnce you have decided on a problem to solve using neural networks, you will need to gather data for training purposes. The training data set includes a number of cases, each containing values for a range of input and output variables. The first decisions you will need to make are: which variables to use, and how many (and which) cases to gather.The choice of variables (at least initially) is guided by intuition. Your own expertise in the problem domain will give you some idea of which input variables are likely to be influential. As a first pass, you should include any variables that you think could have an influence - part of the design process will be to whittle this set down.

For example, consider a neural network being trained to estimate the value of houses. Theprice of houses depends cri