Thesis Abstract

Abstract
Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. Unicode Optical Character Recognition (OCR) involves recognizing characters from multiple languages and scripts encoded in the Unicode Standard. In this research project, we propose a neural network-based approach for Unicode OCR, specifically focusing on recognizing characters from diverse languages and scripts. The neural network architecture is designed to handle the complexity of recognizing characters that span various languages such as Latin, Cyrillic, Arabic, Chinese, and more. Through the use of deep learning techniques, the neural network can effectively learn the features and patterns of characters regardless of the language or script they belong to. This approach offers a versatile solution for OCR applications that require the recognition of multilingual and multiscript text. Training the neural network involves a large dataset of annotated Unicode characters from different languages. The dataset is used to teach the neural network to accurately recognize and classify characters based on their visual representations. By leveraging techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the model can learn the spatial hierarchies and sequential dependencies within characters, enabling robust recognition capabilities. The neural network is trained using backpropagation and optimization algorithms to minimize the recognition errors and improve the overall accuracy of the OCR system. Fine-tuning the network parameters and optimizing the training process are essential steps to ensure the model can generalize well to unseen Unicode characters across various languages and scripts. The performance of the proposed neural network for Unicode OCR is evaluated on benchmark datasets containing text samples from multiple languages. The evaluation metrics include accuracy, precision, recall, and F1 score, which provide insights into the model's ability to correctly identify characters from different scripts. The experimental results demonstrate the effectiveness of the neural network in achieving high accuracy rates across diverse languages and scripts, showcasing its potential for real-world applications in multilingual OCR systems. Overall, the neural network-based approach for Unicode OCR presents a robust and adaptable solution for recognizing characters from various languages and scripts encoded in the Unicode Standard. By leveraging the power of deep learning and neural networks, this research contributes to advancing the field of multilingual OCR and lays the foundation for developing more sophisticated and language-independent OCR systems.

Thesis Overview