Rad se bavi tematikom određivanja teksta na osobnim dokumentima u stvarnom vremenu. Detaljno se proučavaju arhitekture i mogućnosti popularnih modela za detekciju objekata u stvarnom vremenu kao i povijest njihovog nastanka. Proučeni modeli koji su korišteni i pri eksperimentalnom određivanju rezultata su: Brži R-CNN, EfficientDet i YOLOv5. Rezultati vrednovanja modela određeni su po uzoru vrednovanja dva najpoznatija izazova u svijetu detekcije objekata: COCO izazov i Pascal VOC izazov. Predložena je programska potpora za mobilni operacijski sustav iOS koja omogućava detekciju ključnih informacija na osobnim dokumentima u stvarnom vremenu. The paper deals with the topic of text detection on personal documents in real-time. The architectures and features of popular models for real-time object detection, as well as the history of their origin, are studied in detail. The studied models that were also used in the experimental determination of the results are: Faster R-CNN, EfficientDet and YOLOv5. The results of the model evaluation were determined according to standardised model evaluation for the two of the most popular challenges in the world of object detection: the COCO challenge and the Pascal VOC challenge. Software support for the iOS mobile operating system is proposed, which enables the detection of key information on personal documents in real-time.