Optimasi Pengenalan Plat Nomor Kendaraan Berbasis Mobile Menggunakan Google ML Kit: Implementasi dan Analisis Akurasi - Mohamamd Hanan Gaffari; Chairani Fauzi (Journal of Information System Research (JOSH), 2026)
Manual vehicle identification in parking systems and access control is still slow, error-prone, and inefficient, while the number of vehicles in Indonesia continues to increase. This study aims to design and implement a mobile-based vehicle license plate recognition system using Google ML Kit Text Recognition on the Android platform. Google ML Kit is used as an OCR SDK with a pre-trained model; therefore, this study does not retrain a CNN model and does not claim to develop a deep learning architecture from scratch. The contribution of this study is to provide an Android-based license plate OCR implementation workflow, integrate recognition results with a Supabase database, and evaluate character-level accuracy on license plate images. The system workflow consists of license plate image acquisition, lightweight client-side preprocessing, on-device text recognition, OCR result normalization, database matching, and character-level accuracy evaluation. The test data consist of 25 license plate images collected from a public Kaggle dataset. The results show that 20 out of 25 plates were read perfectly, while five samples contained partial recognition errors. Of 201 tested characters, 194 were correctly recognized, resulting in an average character recognition accuracy of 96.5%. Recognition errors were mainly affected by similar character shapes, image quality, light reflection, capture angle, and blur. These results indicate that Google ML Kit can be applied as a practical mobile OCR solution, although the validation remains limited by the small number and limited variation of test samples.