Advancements in OCR: A Deep Learning Algorithm for Enhanced Text Recognition
Parikshit Sharma, Department of Mathematics, Birla Institute of Technology and Science, Pilani (Rajasthan), India.
Manuscript received on 22 July 2023 | Revised Manuscript received on 04 August 2023 | Manuscript Accepted on 15 August 2023 | Manuscript published on 30 August 2023 | PP: 1-7 | Volume-10 Issue-8, August 2023 | Retrieval Number: 100.1/ijies.F42630812623 | DOI: 10.35940/ijies.F4263.0810823
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Optical Character Recognition (OCR) has significantly evolved with the rise of deep learning techniques. In this research paper, we present a novel and advanced OCR algorithm that leverages the power of deep learning for improved text recognition accuracy. Traditional OCR methods have faced limitations in handling complex layouts, noisy images, and diverse fonts, affecting overall performance. Our proposed algorithm addresses these challenges through the integration of deep neural networks, specifically convolutional and recurrent layers. The algorithm undergoes comprehensive training on large-scale datasets, enabling it to learn intricate patterns and features, resulting in robust recognition capabilities. Furthermore, we introduce an attention mechanism that enhances the model’s ability to focus on critical text regions, enhancing accuracy and efficiency. Through extensive experiments and evaluations on benchmark datasets, we demonstrate the superiority of our deep learning-based OCR algorithm over conventional approaches. Our algorithm achieves state-of-the-art performance on various OCR tasks, including multilingual text recognition and document digitization. Additionally, we conduct an in-depth analysis of the algorithm’s behaviour under various scenarios, such as low-resolution inputs and challenging environmental conditions. The findings from this research not only contribute to the field of OCR but also open avenues for applications in document analysis, text extraction, and content digitization in real-world scenarios. The integration of deep learning in OCR showcases its potential in revolutionising text recognition tasks, pushing the boundaries of accuracy and efficiency in this domain.
Keywords: OCR, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Attention Mechanism, Text Recognition, Document Analysis.
Scope of the Article: Deep Learning