Detecting and Reading QR Codes Using Computer Vision
Blog post from Roboflow
A Quick Response (QR) code is an optical label that can be read by a computer to access data about an object, often directing users to websites or applications through encoded information using numeric, alphanumeric, byte, or kanji modes. The structure of a QR code includes key components such as the quiet zone, finder pattern, alignment pattern, timing pattern, version information, and data cells, which collectively enable accurate data retrieval. The blog post discusses how QR codes can be detected and read using computer vision techniques, with the help of the Roboflow platform that facilitates dataset collection, model training, and deployment. By utilizing Roboflow's tools and APIs, users can train models to identify QR codes in images and decode their contents efficiently. The process includes collecting diverse QR code datasets, annotating them, training a detection model, and deploying it to make inferences and extract the region of interest (ROI) for further data reading using libraries like pyzbar. The blog also encourages exploring Roboflow Universe for datasets to spur creativity in developing various computer vision projects.