Advancing Optical Character Recognition (OCR) with Transformer-Based Architectures

Abstract

This paper provides a comprehensive review of advancements in Optical Character Recognition (OCR) technology, focusing on recent algorithmic improvements and practical applications. It covers the evolution from traditional OCR techniques to modern deep learning approaches, highlighting innovations such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that enhance text extraction accuracy and efficiency. The paper also explores the integration of OCR with artificial intelligence (AI) and natural language processing (NLP) for improved performance in diverse applications like document digitization and automated data entry. Challenges such as handling diverse fonts, text layout variations, and image quality issues are discussed, along with potential future directions for advancing OCR technology.