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Neural Network for Unicode Optical Character Recognition A Case Study of DHL Enugu
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Software Implementation for Neural Network for Unicode Optical Character Recognition (A Case Study of DHL, Enugu)

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Reference ID: SD-5050-CS

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DEDICATION

This research work titled "Neural Network for Unicode Optical Character Recognition" is dedicated to God for his enabling grace and to all computer enthusiasts who help to make life a pleasant experience.



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ACKNOWLEDGEMENT

I owe my indebtedness to my Supervisor (Name of your Supervisor), the Head of Department (Name of your HOD), the Lecturers in the department of Computer Science Education, Book Authors and Profound Scholars of existing/related research work for your moral support that facilitated the successful completion of my (Tertiary Institution level). I am grateful to God Almighty and my parent for their financial support in my career. I really appreciate you all for everything, Thank you very much.



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Neural Network for Unicode Optical Character Recognition (A Case Study of DHL, Enugu)

TABLE OF CONTENTS

Preliminary Pages

Chapter One

Introduction

  • 1.1 Background …

Chapter Two

Literature Review

  • 2.1 Introduction

Chapter Three

Research Methodology

  • 3.1 Introduction

Chapter Four

Results and Discussion

  • 4.1 Introduction

Chapter Five

Summary, Conclusion, and Recommendation

  • 5.1 Introduction
  • 5.2 Summary
  • 5.3 Conclusion
  • 5.4 Recommendation

References




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ABSTRACT

Optical character Recognition (OCR) refers to the process of converting printed tamil text documents into software translated Unicode tamil text. The printed documents available in the form of books, projects, magazines etc are scanned using standard scanners which produce an image of the scanned documents. As part of the preprocessing phase the image like is checked for skewing. If the image is skewed, it is corrected by a simple rotation technique in the appropriate direction. Then the image is passed through a noise elimination phase and is binarized. The preprocessed image is segmented using an algorithm which decomposes the scanned text into paragraphs using special space detection technique and then the paragraphs into lines using vertical histograms, and lines into words using horizontal histograms, and words into character image glyphs using horizontal histograms.

Each image glyph is comprised of 32 x 32 pixels, thus a data base of character image glyphs is created out of the segmentation phase. Then all the image glyphs are considered for recognition using Unicode mapping. Each image glyph is passed through various routines which extract the features of the glyph. The various features that are considered for classification are the character height, character width, then number of horizontal lines (Long and short), the number of vertical lines (long and short), the horizontally oriented curves, the vertically oriented curves, the number of circles, number of slope lines, image centroid and special dots. The glyphs are now set ready for classification based on these features. The extracted features are passed to a support vector machine (SVM) where the characters are classified by supervised learning algorithm. These classes are mapped into Unicode for recognition. Then the text is reconstructed using Unicode fonts.





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Neural Network for Unicode Optical Character Recognition (A Case Study of DHL, Enugu)

CHAPTER ONE

1.0 Introduction

Character is the basic building block of any language that is used to build different structures of a language. Characters are the alphabets and the structures are the words, strings and sentences.

Optical character Recognition (OCR) is the process of converting an image of text, such as a scanned project character, document or electronic fax file, into computer-editable text. The text in an image is not editable. The letters/characters are made of tiny dots (pixels) that together form a picture of text. During OCR, the software analyzes an image and converts the pictures of the characters to editable text based on the patterns of the pixels in the image. After OCR, you can expert the converted text and use it with a variety of word-processing, page layout and spreadsheet applications. OCR also enables screen readers and refreshable bralle displays to read the text contained in images.

Optical character Recognition (OCR) deals with machine recognition of characters present in an input image obtained using scanning operation. It refers to the process by which scanned images are electronically processed and converted to an editable text. The need for OCR arises in the context of digitizing tamil documents from the ancient and old era to the latest, which helps in sharing the data through the internet.

A properly printed document is chosen for scanning. It is placed over the scanner, A scanner software is invoked which scans the document. The document is sent to a program that saves it in preferably TIF, JPG or GIF format, so that the image of the document can be obtained when needed. This is the first step in OCR (Vijaya Kumar, 2001), the size of the input image is as specific by the user and can be of any length but is inherently restricted by the scope of the vision and by the scanner software length.

This is the first step in the processing of scanned image. The scanned image is checked for skewing, there are possibilities of image getting skewed with either left or right orientation.

Here, the image is first brightened and binarized the function for skew detection checks for an angle of orientation between +15 degrees and if detected than a simple image rotation is carried out till the lines match with the true horizontal axis, which produce a skew corrected image.

After pre-processing, the noise free image is passed to the segmentation phase, where the image is decomposed into individual characters.

CHAPTER TWO

2.0 Literature Review

2.1 Introduction

This chapter focuses on the review of related literature. A literature review includes the current knowledge as well as theoretical and methodological contributions to a particular topic. It documents the state of the art with respect to the topic you are writing. It surveys the literature in the topic selected. In this research work the literature review includes the …

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Final year research work is all about finding real life problem and proffering solution that will partially or totally eliminate the existing system bottlenecks. The following are the major and elective project proposal writing sections for "Neural network for unicode optical character recognition (a case study of dhl, enugu)" research work;

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  • Motivation for Embarking on the Project

  • Brief Background of Study

  • Statement of Problems

  • Aim of the Study

  • Specific Objectives of the Study

  • Significance of the Study (Who benefits from the project and how?)

Elective Sections
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Proposal Writing Format for Computer Science Education Research Work


Neural Network for Unicode Optical Character Recognition is a proposal topic for final year research work, which comprises the major and elective project proposal writing sections for Neural Network for Unicode Optical Character Recognition (A Case Study of DHL, Enugu) research work.

Major Sections
  • Motivation for Embarking on the Project

  • Brief Background of Study

  • Statement of Problems

  • Aim of the Study

  • Specific Objectives of the Study

  • Significance of the Study (Who benefits from the project and how?)

Elective Sections
  • Relevant Research Questions

  • Relevant Research Hypotheses


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CHAPTER ONE

  • Motivation / Statement of Problems
  • Aims & Objective of Study
  • Scope of Study
  • Significance of Study

CHAPTER TWO

  • State two or more citation from your review of related literature.

CHAPTER THREE

  • Know the methodologies, tools and techniques used.

CHAPTER FOUR

  • Justification of your work and things to adhered to before using the system or research work.

CHAPTER FIVE

  • Conclusion and Recommendation

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