UniDataPro/synthetic-printed-japanese-passports
Japanese passport dataset Dataset contains 5,000+ photos of synthetic Japanese passports, designed for training and validating Machine Learning models in PII extraction and document analysis. It features identity documents from a wide range of different countries and other countries, simulating the variety encountered in international travels. By utilizing this synthetic dataset, researchers and businesses can advance their capabilities in biometric security, identity… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/synthetic-printed-japanese-passports.
Japanese passport dataset
Dataset contains 5,000+ photos of synthetic Japanese passports, designed for training and validating Machine Learning models in PII extraction and document analysis. It features identity documents from a wide range of different countries and other countries, simulating the variety encountered in international travels.
By utilizing this synthetic dataset, researchers and businesses can advance their capabilities in biometric security, identity verification, and data protection technologies. - [Get the data](https://unidata.pro/datasets/synthetic-printed-japanese-passports/?utm_source=huggingface&utm_medium=referral&utm_campaign=synthetic-printed-japanese-passports)
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Each high-quality image features realistic passport numbers, names, and other biographical data.
Example of the data
Researchers can utilize this dataset to explore detection methods and algorithms for electronic systems, aiming to improve authentication processes and prevent identity fraud.
Frequently Asked Questions
Why use synthetic passport images instead of real identity documents?
Synthetic documents enable machine learning research without exposing personal information or handling sensitive government-issued IDs.
How does the metadata support machine learning experiments?
The metadata allows researchers to analyze model performance under specific acquisition conditions rather than relying only on overall accuracy. Models can be evaluated across different lighting environments, viewing angles, capture distances, and demographic categories to identify systematic failure cases.