ELP 1.0: A Comprehensive and Geographically Diverse Dataset of European License Plates

For a few decades, automatic license plate recognition (ALPR) has been considered as a powerful intelligent video analytics tool to deal with the growing number of vehicles worldwide. Most recent ALPR landmark achievements are contingent on access to diverse and rich datasets for model training and evaluation. In this context, we present a naturalistic dataset of European license plates, named ELP 1.0. Unique to ELP 1.0 is its vast geographical coverage, comprising a wide spectrum of European countries (20) and vehicle categories (10). Using the proposed dataset, we benchmark the performances of several baseline deep learning models, assessing their ability to detect and recognize license plates. Further, to picture the ELP 1.0 dataset’s features, we propose a try-one-subset-out setup and evaluate the generalization abilities of ALPR approaches across an array of scenarios. The experimental findings highlight ELP 1.0 challenges as well as its usefulness to question ALPR models’ capacities. The ELP 1.0 dataset, with its comprehensive annotations, features, and benchmark results, will serve as a testbed to support future ALPR research and, more broadly, intelligent transportation industrial solutions. The ELP 1.0 dataset is accessible upon request via this URL.