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Commercial
Synthetic USA Driver License Dataset
Features high-quality, AI-generated images of U.S. driver licenses from multiple states, including California, Texas, and New York. Designed for OCR, data extraction, and identity verification model training, this synthetic ID dataset ensures realistic detail, balanced demographics, and secure handling of personally identifiable information.
-
- Images
- 5 000
- PII
- Data generation
- Security
- Anti-spoofing
- Computer Vision
- PII
- Data generation
- Security
- Anti-spoofing
- Computer Vision
Features high-quality, AI-generated images of U.S. driver licenses from multiple states, including California, Texas, and New York. Designed for OCR, data extraction, and identity verification model training, this synthetic ID dataset ensures realistic detail, balanced demographics, and secure handling of personally identifiable information.
- PII
- Data generation
- Security
- Anti-spoofing
- Computer Vision
-
- Images
- 5 000
Dataset Info
| Characteristic | Data |
| Description | Printed synthetic driver license images for training ML models in PII extraction |
| Data types | Image |
| Tasks | OCR, Computer Vision |
| Total number of files | 5 000 |
| Number of files in a set | 96 (Angles - 3, Lighting - 4, Backgrounds - 4, Distances - 2) |
| States | California, Florida, New York, Pennsylvania, Texas |
| Angles | 0°, 25°, 45° |
| Lighting | Natural-daylight, Office-LED, Warm-indoor, Dim-light |
| Backgrounds | Neutral wall, Textured desk, Outdoor pavement, Docs-on-docs |
| Distance | Close (80-90 % frame), Medium (50-60 %) |
| Labeling | Metadata (License ID, Sample ID, Class, Gender, Age Group, Angle, Distance, Category, Resolution, Camera, Light Condition, Background, Timestamp) |
| Gender | Male, Female |
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Technical
Characteristics
| Characteristic | Data |
| Image Extensions | JPNG |
| Data Type | generated |
Dataset Use Cases
FAQs
What types of annotations are provided?
Each image includes metadata fields such as License ID, Gender, Class, Age Group, Angle, Distance, and Lighting Condition. These detailed annotations improve model performance for OCR, text recognition, and data extraction tasks involving driver license imagery.
Can I request a sample of the dataset before purchasing?
Yes. Unidata provides free dataset samples for evaluation and testing purposes. These samples allow developers to assess data quality, structure, and labeling consistency before purchasing a dataset.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free samples are available for preliminary testing, while full versions of our datasets are sold for commercial and research use, ensuring both accessibility and responsible data management.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. Unidata datasets comply with GDPR and international data protection laws. Since this dataset is synthetic, it contains no real personal data and adheres to ethical standards for privacy and data safety.
How are Unidata datasets stored?
All datasets are securely stored on AWS cloud servers to ensure high availability and compliance with ISO 27001 and ISO 27701 standards. This guarantees data integrity, scalability, and secure access for customers handling sensitive AI training material.
How long does it take to receive the dataset?
After submitting a purchase request, our team confirms details and provides documentation for approval. Once payment and agreement are complete, the dataset is delivered securely within 3–10 business days.
Is this a real-world dataset or synthetic data?
This is a synthetic dataset created to replicate real driver licenses while maintaining full data privacy. It enables safe model training for identity recognition, document verification, and text extraction without using sensitive or personal information.
Why use synthetic driver license images instead of real identity documents?
Synthetic driver license datasets eliminate privacy risks while preserving the visual complexity needed for AI training. Because no real personal data is included, organizations can develop and validate OCR, document understanding, and identity verification systems using legally safer training data with consistent annotations and balanced scenarios.
Still have questions about using Unidata datasets?
Read our user-guides
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