Automated labeling is a process in machine learning and data preparation where objects or features of interest within data, such as images, text, or audio, are identified and annotated using algorithms or AI models. This eliminates the need for manual labeling, saving significant time and effort, especially for large datasets. Automated labeling can include tasks like identifying objects in images, tagging text with categories, or marking specific regions of interest in documents. It is often used to prepare datasets for training machine learning models, ensuring consistent and scalable annotations while reducing human error. 

Labeling and dataset creation of Vehicle Registration Certificates 

Regarding vehicle registration certificate data (OE), 19 pieces of information—9 on the front side and 10 on the back- are identified and labeled. We used a technique for creating realistic datasets without compromising privacy, which involves replacing real data with synthetic or pseudo-anonymized data in vehicle registration certificates. Critical fields, such as names, registration numbers, and dates, are substituted with randomized or pseudonymous values while maintaining the document’s structure and variability. This approach ensures the dataset reflects real-world conditions, allowing machine learning models to train effectively without using sensitive client information.