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Neural network for unicode optical character recognition

 

Table Of Contents

<p> </p><p><br>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

Neural networks have shown great promise in the field of Optical Character Recognition (OCR) for extracting text from images. This research focuses on the development of a neural network specifically designed for Unicode OCR, which involves recognizing and converting characters from a wide range of languages and scripts. The proposed neural network architecture utilizes convolutional neural network (CNN) layers for feature extraction and a recurrent neural network (RNN) for sequence modeling. This combination allows the network to effectively capture both spatial information within individual characters and temporal dependencies between characters in a sequence. The CNN layers are responsible for extracting high-level features from input images, while the RNN component processes these features in a sequential manner to recognize characters in the correct order. To train the neural network, a large dataset of labeled Unicode characters is used. The dataset consists of diverse characters from different languages and scripts, ensuring that the network can generalize well to a wide range of inputs. The training process involves feeding images of characters into the network and adjusting the network parameters to minimize the difference between the predicted and actual characters. Once trained, the neural network can be used to recognize Unicode characters from images with high accuracy. The network takes an input image containing one or more characters and processes it through the CNN and RNN layers to generate a sequence of predicted characters. Post-processing techniques such as language modeling and beam search can be applied to improve the accuracy of the final output. The performance of the neural network is evaluated using metrics such as accuracy, precision, and recall on a separate test dataset. The results demonstrate that the proposed neural network architecture achieves state-of-the-art performance in Unicode OCR tasks, outperforming existing methods on a variety of languages and scripts. In conclusion, this research presents a novel neural network architecture for Unicode optical character recognition that combines CNN and RNN components to effectively recognize characters from diverse languages and scripts. The experimental results show that the proposed network achieves high accuracy and outperforms existing methods in recognizing Unicode characters from images. This work contributes to the advancement of OCR technology for multilingual text extraction and has potential applications in document digitization, language translation, and other text processing tasks.

Project Overview

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