Optical Text

Optical text recognition (OCR) research focuses on accurately extracting text from images, encompassing diverse scripts and challenging conditions like ancient documents or complex scene imagery. Current efforts leverage deep learning architectures, particularly transformer-based models and efficient parameterization techniques like LoRA, to improve accuracy and reduce computational demands across multiple text domains and languages. This work is crucial for digitizing historical archives, improving accessibility for low-resource languages, and enabling applications in fields ranging from medical image analysis to autonomous driving. The development of robust and adaptable OCR models is driving progress in both fundamental computer vision and numerous practical applications.