OCR Annotation

OCR annotation focuses on improving the accuracy and efficiency of Optical Character Recognition (OCR) systems, primarily by creating high-quality training datasets and developing advanced models for tasks like text localization, structure recognition (e.g., tables), and post-OCR correction. Current research emphasizes multimodal approaches, integrating visual and textual information using transformer-based architectures and incorporating weakly supervised learning techniques to reduce annotation costs. These advancements are crucial for various applications, including document understanding, defect detection in manufacturing, and analysis of complex visual documents like comics, significantly impacting fields ranging from digital humanities to industrial automation.