DEVELOPMENT OF A PATTERN RECOGNITION SYSTEM FOR THE RECOGNITION OF NUMBERS FROM 0-9
| Format: Ms Word | 1-5 Chapters | Table of Content|
INSTANT PROJECT MATERIAL DOWNLOAD
Study Level: BTech, BSc, BEng, BA, HND, ND or NCE
Amount: ₦3,000.00
ABSTRACT
Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe e.g. I recognize a face, I understand spoken word, I read handwriting, I distinguish fresh food from its smell etc. We would like to give similar capabilities to machines. This study concerns the development of a pattern recognition system for the recognition of numbers between the ranges of 0-9. Visual Basic 6.0 enterprise edition is used for the software while MS Access is used for the knowledge base.
TABLE OF CONTENTS
Certification – – – – – – – – – ii
Dedication – – – – – – – – – iii
Acknowledgment – – – – – – – – iv
Abstract – – – – – – – – – v
Table of Contents – – – – – – – – vi
CHAPTER ONE: GENERAL INTRODUCTION
1.1 Introduction – – – – – – – – 1
1.2 Background of the Study – – – – – 1
1.3 Statement of the problem – – – – – – 3
1.4 Purpose of the study – – – – – – 3
1.5 Objectives of the study – – – – – – 3
1.6 Significance of the study – – – – – – 4
1.7 Scope of the study – – – – – – – 4
1.8 Limitation of the study – – – – – – 4
1.9 Definition of terms – – – – – – – 5
1.9.1 Pattern recognition – – – – – – – 5
1.9.3 Classifier – – – – – – – – 5
1.9.4 Pattern classifier – – – – – – – 5
1.9.5 Inference engine – – – – – – – 5
CHAPTER TWO: REVIEW OF RELATE LITERATURE
2.1 Introduction – – – – – – – – 6
2.2 Review of Related Literature – – – – – 6
2.3 Pattern Recognition – – – – – – 7
2.4 Planning a Pattern Recognition System – – – – 11
3.5 Probabilistics pattern recognizer – – – – – 12
2.5 Application of pattern recognition system – – – 14
2.6 The classification of pattern recognition system – – 16
CHAPTER THREE: SYSTEM DESIGN
3.1 Introduction – – – – – – – – 17
3.2 Research methodology – – – – – – 17
3.3 System Architecture – – – – – – 18
3.4 Knowledge base – – – – – – – 18
3.5 Inference engine – – – – – – – 19
3.6 Pattern classifier – – – – – – – 20
3.6.1 User interface – – – – – – – 21
3.7 System flow diagram – – – – – – 22
3.7.1 Program flowchart – – – – – – – 23
3.8 Program listing – – – – – – – 23
CHAPTER FOUR: SYSTEM IMPLEMENTATION AND DOCUMENTATION
4.1 Introduction – – – – – – – – 24
4.2 System implementation – – – – – – 25
4.3 Implementation procedure – – – – – 27
4.4 System requirements – – – – – – 28
4.5 System evaluation – – – – – – – 28
4.6 System maintenance – – – – – – 29
4.7 System testing – – – – – – – 29
CHAPTER FIVE: SUMMARY, RECOMMENDATIONS AND
CONCLUSION
5.1 Introduction – – – – – – – 30
5.2 Summary – – – – – – – – 30
5.2 Recommendations – – – – – – – 31
5.3 Conclusion – – – – – – – – 30
References – – – – – – – – 32
CHAPTER ONE
GENERAL INTRODUCTION
1.1 INTRODUCTION
A pattern is an entity, vaguely defined, that could be given a name, e.g.,
- Fingerprint image
- Handwritten word
- Human face
- Speech signal
- DNA sequence
Pattern recognition is the study of how machines can
- Observe the environment
- Learn to distinguish patterns of interest
- Make sound and reasonable decisions about the categories of the patterns.
1.2 BACKGROUND OF THE STUDY
In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is “spam” or “non-spam”). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence. Expert system is a current and major segment of artificial intelligent system that is involved in solving complex professional problems that requires human experts. It is the software systems developed using different techniques of artificial intelligent that can act parallel to the “human” experts. The main role is consultative. These are intelligent information systems that use more than 2000 different rules and are capable to explain the decision. Considering the exiting experts systems, many medical expert systems assist the physicians in making diagnosing which may shorten the time spent in making correct diagnosing errors. At the same time, physician may obtain the information on the symptom of each of the diseases and pathologic syndromes contained therein (Rezers et al., 1984).
In the wise, pattern recognition is considered as the knowledge representation that coordinates the diagnosis. In machine learning, pattern recognition is the assignment of some sort of output value (or La6el) to a given input value (or instance), according to some specific algorithm.
1.3 STATEMENT OF THE PROBLEM
We are often influenced by the knowledge of how patterns are modeled and recognized in nature when we develop pattern recognition algorithms. Research on machine perception also helps us gain deeper understanding and appreciation for pattern recognition systems in nature. Yet, we also apply many techniques that are purely numerical and do not have any correspondence in natural systems.
1.4 PURPOSE OF THE STUDY
The aim of this study is to develop a pattern recognition system based on numbers rather than on any other parameters. The system will recognize and ‘identify’ numbers based on their characteristic features. This will then help the reader to hilly appreciate the difference between natural systems and pattern recognition based on machine learning.
1.5 OBJECTIVES OF THE STUDY
The following are the objectives of the study
– To study the effect of pattern recognition techniques in our daily life as well as the 21st century technology.
– To relate this to the principle of machine learning
– And to design a pattern recognition system that will recognize various forms of numbers between the range of zero (0) to nine (9’).
1.6 SIGNIFICANCE OF THE STUDY
Pattern recognition techniques find app1ictions in many areas: machine learning, statistics, mathematics, computer science, biology, etc. It is hoped that this project will help the reader acquire more understanding of this fascinating subject.
1.7 SCOPE OF THE STUDY
This study is limited to the development of a pattern recognition system for the recognition of numbers from 0 – 9.
1.8 LIMITATION OF THE STUDY
The following are the limiting factors that affect the study:
Time Constraint: Because of the limited time frame, the researcher did not have enough time to explore the research work which limits the scope work.
Lack of Finance: Due to the payment of fee and other academic requirement, t ere was no enough financial resources to carry out the research work.
1.9 DEFINITION OF TERMS
1.9.1 Pattern Recognition
This is the assignment of a label to a given input value. 1.9.2 Pattern K pattern is an entity, vaguely defined, that could he given a name V.
1.9.3 Classifier
In this first approach, the classifier consists in applying thresholds on the hillas parameters (or a combination of these parameters) computed on the incoming images in order to distinguish gamma signatures between all collected images.
1.9.4 Pattern Classifier
Pattern recognition has to do with the assignment of some sort of output value (or Label) to the series of input value (for instance), according to some specific algorithm.
1.9.5 Inference Engine
The process of drawing conclusions from existing data is called inference.
USE THIS MATERIALS AS A GUIDE FOR YOUR PERSONAL RESEARCH WORK (IF PROPERLY CITED)
PAY ₦3,000 HERE TO DOWNLOAD MATERIALS
Account Number: 0709546102
Access Bank: Savings
Account Name: Emmanuel Idorenyin Samuel.