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Neural Network for Unicode Optical Character Recognition

Neural Network for Unicode Optical Character Recognition

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DEDICATION

This research material, titled “Neural Network for Unicode Optical Character Recognition” is dedicated to God for His boundless grace and guidance. It is also a tribute to all computer enthusiasts whose contributions made my research journey smoother and enriched my documentation process, making the experience truly fulfilling.



ACKNOWLEDGEMENT

I am profoundly grateful to everyone who contributed to the successful completion of this project. I am especially grateful to my Supervisor (Name), the Head of Department (Name), and the Lecturers in the Department of Computer Science Education for their invaluable guidance and support. I also acknowledge the contributions of authors and scholars whose works on Neural Network for Unicode Optical Character Recognition provided essential insights. Special thanks go to my study area (and any funding organizations, if applicable) for their financial assistance. I am equally thankful to stakeholders, including mentors, teachers, and colleagues, for their encouragement and support. Finally, I deeply appreciate my family and friends for their patience and unwavering support throughout this journey. Your contributions have been instrumental in making this research a reality.



TABLE OF CONTENTS

PRELIMINARY PAGES

CHAPTER ONE

INTRODUCTION

    CHAPTER TWO

    LITERATURE REVIEW

    • 2.1 Introduction
    • 2.2 Conceptual Review
    • 2.3 Theoretical Framework
    • ⋮
    • 2.4 Empirical Studies
    • 2.5 Research Gaps
    • 2.6 Summary of Literature Review

    CHAPTER THREE

    RESEARCH METHODOLOGY

    • 3.1 Introduction
    • 3.2 Research Design
    • 3.3 Population of Study
    • 3.4 Sampling and Sampling Technique
    • 3.5 Validation of Research Instrument
    • 3.6 Method of Data Collection
    • 3.7 Method of Data Analysis
    • 3.8 Questionnaire Administration
    • 3.9 Ethical Consideration
    • 3.10 Statistical Analysis

    CHAPTER FOUR

    DATA ANALYSIS, RESULT AND DISCUSSION

    • 4.1 Introduction
    • 4.2 Presentation and Analysis of Data
    • 4.3 Re-statement of Research Questions
    • 4.4 Test of Hypotheses
    • 4.5 Discussion of Findings

    CHAPTER FIVE

    SUMMARY, CONCLUSION AND RECOMMENDATION

    • 5.1 Introduction
    • 5.2 Summary of Findings
    • 5.3 Conclusion
    • 5.4 Recommendation
    • 5.5 Suggestion for Further Study

    REFERENCES

    APPENDIX A - “QUESTIONNAIRE”


    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.


    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 conceputal review, theoretical framework, the review of related literature …

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    In preparation for defending a project or seminar on Neural Network for Unicode Optical Character Recognition, it is imperative that as a nursing student, you demonstrate comprehensive knowledge of your research. The defense process is structured to include presenting your work, answering questions, and illustrating its pertinence. Initially, provide a succinct yet thorough introduction to your research topic, emphasizing its importance and the objectives, ensuring that both the audience and the External Examiner can understand the scope of your study.

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