Ambulance Body Text and Signage OCR Recognition Dataset

The current medical industry faces challenges in the slow recognition speed and insufficient accuracy of ambulance information, especially in emergencies where rapid access to body information is necessary. Existing OCR solutions perform poorly under complex backgrounds and different lighting conditions, often leading to misreading or missing information. This dataset aims to solve these technical issues by providing diverse ambulance body images and their corresponding signage and text to improve OCR system recognition performance. Data collection was carried out using high-resolution cameras in various environments, including urban streets and around hospitals, to ensure image diversity. For quality control, we implemented multiple rounds of annotation and expert review to ensure data accuracy and consistency. Data is stored in JPEG format, accompanied by JSON files providing annotation information, facilitating subsequent processing and analysis.