License Plate Images Generation with Diffusion Models

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dc.contributor.authorShpir, Mariiaen_US
dc.contributor.authorShvai, Nadiyaen_US
dc.contributor.authorNakib, Amiren_US
dc.date.accessioned2025-01-29T12:51:17Z
dc.date.available2025-01-29T12:51:17Z
dc.date.issued2024
dc.description.abstractDespite the evident practical importance of license plate recognition (LPR), corresponding research is limited by the volume of publicly available datasets due to privacy regulations such as the General Data Protection Regulation (GDPR). To address this challenge, synthetic data generation has emerged as a promising approach. In this paper, we propose to synthesize realistic license plates (LPs) using diffusion models, inspired by recent advances in image and video generation. In our experiments a diffusion model was successfully trained on a Ukrainian LP dataset, and 1000 synthetic images were generated for detailed analysis. Through manual classification and annotation of the generated images, we performed a thorough study of the model output, such as success rate, character distributions, and type of failures. Our contributions include experimental validation of the efficacy of diffusion models for LP synthesis, along with insights into the characteristics of the generated data. Furthermore, we have prepared a synthetic dataset consisting of 10,000 LP images, publicly available at https://zenodo.org/doi/10.5281/zenodo. 13342102. Conducted experiments empirically confirm the usefulness of synthetic data for the LPR task. Despite the initial performance gap between the model trained with real and synthetic data, the expansion of the training data set with pseudolabeled synthetic data leads to an improvement in LPR accuracy by 3% compared to baseline.en_US
dc.identifier.citationShpir M. License Plate Images Generation with Diffusion Models / Mariia Shpir, Nadiya Shvai, Amir Nakib // 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) / ed.: U. Endriss [et al.]. - [S. l.] : IOS Press, 2024. - P. 4594-4601. - (Frontiers in Artificial Intelligence and Applications ; vol. 392). - https://doi.org/10.3233/FAIA241053en_US
dc.identifier.urihttps://doi.org/10.3233/FAIA241053
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/33368
dc.language.isoenen_US
dc.relation.source27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)en_US
dc.statusfirst publisheden_US
dc.subjectlicense plate recognition (LPR)en_US
dc.subjectsynthetic data generationen_US
dc.subjectsynthetic dataen_US
dc.subjectdiffusion models for LP synthesisen_US
dc.subjectсonference materialsen_US
dc.titleLicense Plate Images Generation with Diffusion Modelsen_US
dc.typeConference materialsen_US
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