Transparency in ANPR Data Collection: Upholding Privacy and Building Trust

Transparency in ANPR Data Collection: Upholding Privacy and Building Trust


In the pursuit of improving ANPR (Automatic Number Plate Recognition) models, transparency emerges as a fundamental principle to uphold privacy and build trust. This article emphasizes the commitment to transparency in data collection practices, addressing key aspects such as lawful basis, types of data collected, data processing, retention, security, data sharing, individual rights, and stakeholder engagement. By ensuring openness with both customers and the general public, the aim is to foster trust, demonstrate compliance with legal requirements, and promote understanding of the responsible use of ANPR technology. Upholding transparency stands as a cornerstone in safeguarding privacy and nurturing a relationship of trust with all stakeholders involved.

Purpose of data collection:

To improve our ANPR (Automatic Number Plate Recognition) and object detection AI models, which are utilized to identify vehicles that appear on our customers' driveways. The acquisition of a large number of images is essential to account for the variations in external factors that can affect image quality and settings, thereby ensuring accurate results from the ANPR system.

Lawful basis:

The processing of personal data for the aforementioned purpose is grounded in the public interest and the fulfilment of a task carried out in the public domain. Moreover, we have a legitimate interest in processing this data as it enables us to provide effective and precise ANPR services to our customers.

Types of Data Collected:

In our pursuit of diversity, we randomly collect images of vehicles on public roads, encompassing the vehicles themselves and their license plates. Unless necessary for a specific purpose, no additional personally identifiable information is collected. We remain committed to updating this policy if any additional data collection becomes imperative. Our focus lies in training the ANPR model to recognize consistent patterns across the images, such as the vehicles and their license plates, while excluding extraneous background details.

Data Processing:

Upon collection, the images are securely transferred to our local, scOS server. A meticulous hygiene process ensues, where pictures failing to meet the intended purpose are promptly deleted. This may occur due to factors such as image blurriness or instances where vehicles are not clearly visible. Subsequently, our dedicated scOS team members manually annotate the images by drawing bounding boxes around vehicles and license plates. This annotation process assists the AI model in identifying the relevant areas of interest within an image. Once all collected images have been annotated, they are processed through the AI model for training, either on a local machine or via secure transfer to our high-performance AWS instance, ensuring expedited processing capabilities.

Data retention:

Given the extensive efforts required to gather a comprehensive dataset encompassing the necessary variations, it is essential for us to retain the collected images while continually improving our model. The retention period is limited to no longer than two years from the date of collection, thereby striking a balance between model enhancement and adhering to responsible data management practices.

Data security:

We accord utmost priority to the security of the collected data. All data is securely stored on our encrypted local server, safeguarding against unauthorized access. Where required, data is securely transmitted to our AWS instance, which adheres to stringent industry standards for data protection. During the manual annotation process on our machines, data handling is executed with the utmost confidentiality and in compliance with appropriate security protocols to maintain data integrity.

Data sharing:

As part of our sensitive approach, we exclusively share data with two authorized Nocrus team members who assist with data annotation and training processes. We do not disclose data to law enforcement agencies or road safety initiatives.

Individual rights:

We respect the rights of individuals under the GDPR. Users possess the right to access their personal data held by us, request rectification of any inaccuracies, and request the deletion of their data. For exercising these rights or any inquiries concerning data processing, individuals may contact us at or reach out to us by phone at 020 8050 1095. Prompt responses will be provided, and appropriate actions will be taken in accordance with applicable data protection laws.

Contact information:

For any queries or concerns regarding data protection and privacy practices, individuals can contact us at or call us at 020 8050 1095.

Please also view more readable policies and articles around data in our Data Ethics Zone.

Legal Basis and Changes:

This privacy policy is established in compliance with the requirements of relevant data protection laws, including the GDPR. We undertake periodic reviews of our data processing practices and Data Ethics Zone to ensure ongoing compliance with legal obligations and industry best practices. Any modifications to this policy will be communicated through our website or via direct notifications to users, as mandated by the applicable laws.