Final Year Report: Handwritten Word Recognition By Rahul Thapa Magar[PDF]

This report is the final year report of Mr. Rahul Thapa Magar. He is a graduate of university of Wolverhampton. We will provide you the link to his project very soon. You can download his report by clicking download here button at the end of this page.

  1. Introduction
    1.1. Academic Question
    a. How will the system work and what techniques, tools and technologies will it
    use to extract text from images?
    b. Will the users need to log in for using this system?
    c. What is the accuracy of your system? Will your system performance
    decrease if the image is provided with enough noise?
    d. How will the users get benefited by this system?
    1.2. Aims & Objectives
    1.2.1. Aims
    • To train the model on synthetic dataset.
    • To extract handwritten word not just from paper or electronic documents
    but also from natural scene images.
    • To help the people to store data digitally without copying from the
    1.2.2. Objectives
    • To research on the Internet, Books, Journals, Articles etc.
    • To implement suitable classifiers and algorithm.
    • To build the platform, web application for the people to perform Optical
    Character Recognition (OCR).
    1.3. Brief Details of the Artifact Produced and Background to the project.
    Data has become the most valuable assets in the world. People are storing
    data in both electronic and paper-based format. They need stored data in
    their daily lives to run their businesses. Rewriting those stored data is time
    consuming and unproductive. Traditionally, text recognition has been done
    on document images because of their well suited digitise planner paperbased formats. But when it comes to natural scene images, the accuracy decreases drastically because of their highly variance in appearance and layout in the images. Additionally, natural images are suffered from noises, inconsistent light, occlusions, orientation etc which makes difficult for the classifier to detect and recognize the text in comparison to document images. In the recent years, the advancement came in the field of computer
    vision techniques and the large volume of datasets produced over the last
    decades has made possible to recognize the text form even natural scene
    images. In this project text spotting is done from natural images by
    implementing two techniques i.e. word detection followed by word
    recognition. This project does not perform character recognition instead it
    recognizes word through word spotting mechanism. The detector is built
    with Tesseract and OpenCV and recognition is done by Convolutional
    Neural Network (CNN). CNN is trained on synthetic datasets known as VGG
    synthetic word datasets. This project is based on flask web application
    where the users perform OCR by uploading images in the system.
    Artefact (proposed) to be developed
    Artefact 1
    Image upload
    Artefact 2
    Word Detection
    Artefact 3
    Word Recognition
    1.4. Potential Users
    There are no specific users required to use this system. Everyone can utilize
    this system to perform OCR. Today, the corporates around the world
    upgraded to digital format. For instance, they store the corporate data,
    information etc. in electronic from. Moreover, the people from every field are
    recognizing the importance of OCR because they do not have to go through
    the hassle of copying the whole words from the hard documents. Since its
    development, it has been applied to many fields and still widening its
    horizon. Some of the fields of OCR are Handwriting recognition, Receipt
    Imaging, Legal Industry, Banking, HealthCare, Captcha, Automatic Number
    Plate Recognition, ATMA: android travel mate application etc. It seems
    everybody needed such systems in today’s world where the data has
    become the valuable assets. So, application of OCR cannot be restricted to
    just some fields and some users.
    1.5. Scope and Limitations of the project.
    Text, being consider as the only tools for preserving and communicating
    information. Today’s modern world is designed to interpret and
    communicate using text clues, labels, texts etc. found in the surroundings.
    So, text has been scattered through many images and videos for the
    communication purposes. Extracting such texts from the images and storing
    the information in digital format helps to secure from the damages done by
    the theft of hard documents. Sometimes we need to digitally replicate the
    text of the images. In such cases OCR can play an important role.
    System is based on word recognition method instead of character
    recognition. Unlike the character recognition, which recognize the word by
    the recognition of letters, word recognition has to trained with the whole
    word as input.
    So, the recognition of such model is constraint to the number of words in
    the dictionary because in such method we can cover all the words for
    recognition. Similarly, the accuracy of this method is low because the model
    is trained with small no of datasets. The reason behind small number of
    datasets is because of computational limitations. The other limitation of this
    system are it does not work offline, only recognize the English alphabetical

    1.6. Report Structure

    • Introduction: It provides the overall introduction of the project. It
    includes topic such as project aims, objectives, scope, limitations,
    academic question, and artifact.
    • Literature Review: It includes the necessary information for the
    completion of the project such as background research,
    components, and similar system.
    • Development: This section provides the information from project
    planning to its development. It includes all the planning’s, designs,
    and testing.
    • Answering Academic Question: This section provides the answers
    regarding the academic questions.
    • Conclusion: It concludes the whole project with its future escalation.
    • Critical Evaluation: It includes all the necessary evaluation towards
    the report, systems, and development process.

Click the download button to download the full report.

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