Optical Character Recognition (OCR): A Comprehensive Review
Cutting-edge techniques, such as deep learning-based approaches, transformer architectures, and attention mechanisms, are discussed, highlighting their role in improving accuracy and efficiency in text recognition tasks.
Abstract
Optical Character Recognition (OCR) technology has witnessed significant advancements in recent years, driven by the growing demand for digitizing and processing large volumes of printed and handwritten documents. This paper presents a comprehensive overview of the latest developments in OCR, with a focus on key innovations and breakthroughs. Cutting-edge techniques, such as deep learning-based approaches, transformer architectures, and attention mechanisms, are discussed, highlighting their role in improving accuracy and efficiency in text recognition tasks. The integration of OCR with other emerging technologies, such as natural language processing and computer vision, is also explored, opening new avenues for enhancing OCR capabilities. Additionally, the paper delves into various applications of OCR across domains, including document processing, data extraction, language translation, and accessibility for visually impaired individuals. By understanding these recent advancements, researchers and practitioners can harness OCR's full potential to address diverse challenges in document digitalization and information retrieval processes.