Automatic ID Card Data Detection and Extraction System Based on Convolutional Neural Network and Optical Character Recognition

Abstract

Identity cards in Indonesia serve as official identities that store important information for administrative and social purposes. Managing information on ID cards often faces challenges, especially in manual processes. Implementing OCR technology for ID card data extraction improves operational efficiency and opens up opportunities for developing more innovative digital services. With the automation of data capture, organizations can focus on improving service quality and analyzing community needs more precisely. This research develops a CNN and Optical Character Recognition (OCR)-based ID card detection system to improve data processing efficiency. The system was tested and produced 92% accuracy, 100% precision, 85% recall, and 92% F1-Score. Based on this data, using OCR technology allows text extraction from physical KTP documents with high accuracy, thus speeding up data verification and reducing input errors.