Home / Computer Science / Neural network for unicode optical character recognition

Neural network for unicode optical character recognition

 

Table Of Contents

<p> </p><p>Title page &nbsp; &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; ii</p><p>Certification &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; iii</p><p></p><p>Approval page &nbsp; &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; iv</p><p>Dedication &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; v</p><p>Acknowledgement &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;vi</p><p>Abstract – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; vii</p><p>Table of contents &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; ix</p><p><strong>

Chapter ONE

</strong></p><p><strong>&nbsp;1.0 INTRODUCTION &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;–</strong>&nbsp; &nbsp; &nbsp; &nbsp;1</p><p>1.1 &nbsp; &nbsp; &nbsp;Statement of the problem &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; 5</p><p>1.2 &nbsp; &nbsp; &nbsp;Purpose of the study &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;6</p><p>1.3 &nbsp; &nbsp; &nbsp;Aims and objectives &nbsp; &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;6</p><p>1.4 &nbsp; &nbsp; &nbsp;Scope of study &nbsp; &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;8</p><p>1.5 &nbsp; &nbsp; &nbsp;Limitations of the study – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; 8</p><p>1.6 &nbsp; &nbsp; &nbsp;Definition of terms.- &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; 9</p><p><strong>

Chapter TWO

</strong></p><p><strong>&nbsp;2.0 LITERATURE REVIEW – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp;11</strong></p><p><strong>

Chapter THREE

</strong></p><p>3.0 &nbsp; &nbsp; &nbsp;Methods for fact finding and details discussions on the subject matter. &nbsp; &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; 15</p><p>3.1 &nbsp; &nbsp; &nbsp;Methodologies for fact finding &nbsp; &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;15</p><p></p><p>3.2 &nbsp; &nbsp; &nbsp;Discussions &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;16</p><p><strong>

Chapter FOUR

</strong></p><p>4.0 &nbsp; &nbsp; &nbsp;Futures, Implications and challenges of the subject matter for the society &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;20</p><p>4.1 &nbsp; &nbsp; &nbsp;Futures &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;20</p><p>4.2 &nbsp; &nbsp; &nbsp;Implications &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;21</p><p>4.3 &nbsp; &nbsp; &nbsp;Challenges &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;22</p><p><strong>

Chapter FIVE

</strong></p><p>5.0 &nbsp; &nbsp; &nbsp;SUMMARY, RECOMMENDATION AND CONCLUSION 24</p><p>5.1 &nbsp; &nbsp; &nbsp;Summary &nbsp; &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;24</p><p>5.2 &nbsp; &nbsp; &nbsp;Recommendation &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;25</p><p>5.3 &nbsp; &nbsp; &nbsp;Conclusion &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;28</p><p>References &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp; – &nbsp; &nbsp; &nbsp;– &nbsp; &nbsp; &nbsp; 30</p> <br><p></p>

Project Abstract

Abstract
Optical Character Recognition (OCR) is a technology that enables the translation of various types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. With the increasing digitization of information, OCR has become a crucial tool for converting printed or handwritten text into machine-encoded text. One of the challenges in OCR is the recognition of characters from different languages and character sets, including Unicode characters which cover a wide range of languages and symbols. Neural networks have shown great promise in OCR tasks due to their ability to automatically learn features from input data and adapt to different character variations. In this research project, we focus on the development and optimization of a neural network for Unicode optical character recognition. The primary objective is to create a model that can accurately recognize and classify Unicode characters from various languages and scripts. The neural network architecture is designed to process input images of Unicode characters and output the corresponding Unicode labels. The model consists of multiple layers, including convolutional layers for feature extraction and dense layers for classification. Training the neural network involves feeding it a large dataset of labeled Unicode characters, allowing the model to learn the patterns and characteristics of different characters. To enhance the performance of the OCR system, various optimization techniques are implemented during training. These include data augmentation to increase the diversity of the training dataset, regularization methods to prevent overfitting, and hyperparameter tuning to optimize the model's architecture and training parameters. Additionally, transfer learning is explored to leverage pre-trained neural network models and adapt them to the Unicode OCR task. The evaluation of the neural network model is conducted using standard metrics such as accuracy, precision, recall, and F1 score. The model is tested on a diverse set of Unicode characters to assess its generalization capability across different languages and scripts. The results demonstrate the effectiveness of the neural network in accurately recognizing Unicode characters with high precision and recall rates. Overall, this research contributes to the advancement of OCR technology by specifically addressing the challenges of Unicode character recognition. The developed neural network model offers a robust solution for accurately transcribing Unicode text from a variety of sources, paving the way for improved multilingual OCR applications in real-world scenarios.

Project Overview

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.

Algorithm for Segmentation


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