Text Localization

Text localization focuses on accurately identifying and locating textual information within various data types, including images, audio, and documents, aiming to bridge the gap between raw data and meaningful textual content. Current research emphasizes the development of efficient and robust deep learning models, often employing transformer architectures and techniques like masked image modeling or geometric feature extraction, to improve accuracy and reduce computational costs across diverse scripts and data modalities. These advancements have significant implications for applications such as optical character recognition (OCR), scene text understanding, and document analysis, enabling improved automation and accessibility in numerous fields.