Deep Learning-Based Optical Character Recognition for Robust Real-World Conditions: A Comparative Analysis

This paper offers a comprehensive comparative analysis of Optical Character Recognition (OCR) techniques, spanning from traditional methods to advanced deep learning models such as Transformers, BERT, and Bi-LSTM. OCR serves as a pivotal tool for converting printed or handwritten text into digital formats, facilitating applications in document digitization and text analysis. Our analysis provides valuable insights into the strengths and weaknesses of each approach, thereby elucidating their practicality in real-world scenarios. In the domain of information retrieval, a novel approach entails leveraging bidirectional LSTM for semantic search, harnessing deep neural networks to comprehend textual content nuances beyond mere keyword matching. This study serves as an indispensable resource for researchers, practitioners, and developers interested in OCR technology, furnishing a comprehensive overview of OCR techniques and their applicability across various domains. Furthermore, we delve into the adaptation of Transformer models, including BERT, to the OCR domain, assessing their efficacy in handling diverse text types and discussing their potential to enhance OCR accuracy and robustness. We also explore ensemble techniques that amalgamate multiple OCR approaches to exploit their complementary strengths, thereby augmenting overall performance. Moreover, our exploration extends to emerging trends and future directions in OCR research, encompassing the integration of multimodal information and the exploration of self-supervised learning paradigms for OCR model training. These advancements hold significant promise for further elevating the accuracy, efficiency, and versatility of OCR systems across diverse domains.