ALPR, aka Automated License Plate Recognition, is the process of capturing license plate information. ALPR software takes a picture of a license plate and collects real-time information, often to identify a vehicle. For organizations like police departments, city governments, and vehicle-related industries, ALPR technology can provide information and insights that help optimize communities and processes.

We will be discussing fundamental metrics used to evaluate and compare computer vision systems such as accuracy, recall and precision. We will cover evaluations of the two main branches of computer vision: classification and detection.

In this two part blog, we want to explore how to test a Computer Vision system. We will do a deep dive into the process and best practices for properly and accurately run a computer vision test. The first part here will cover Annotation. Data annotation helps provide valuable insight and information with respect to the system requirements.The second part will cover evaluation and explain topics like accuracy, recall and precision.

Artificial Intelligence can thus be thought of as a system to help mimic human intelligence by using algorithms; techniques, rules and/or methods to learn, reason and react to different situations and scenarios like a human would do.

Companies needing to access computer vision through a cloud API have many choices.

What would an ideal LPR system look like? Automated reading of license plates has been around for decades. Unfortunately, most of the technology has not changed much in that time.

Every year in the UK hundreds gather for the Festival of the Unexceptional. In usually damp surroundings the most unglamorous vehicles of a little-gilded age joust for the prize of most mediocre…