![]() ![]() Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Opinions expressed are solely my own and do not express the views or opinions of my employer. Written by Rebecca Minich, Product Analyst, Data Science at Google. If you’re interested in more projects like this checkout website. If you’re interested in seeing it in action, check it out on YouTube. ![]() Once the hardware and the model were ready to go, Cortex was used to pull the two together to get the predictions in real-time. Keras-OCR was used to detect the characters on the license plates. The final model is available here and the dataset is available here. Yolo3 was chosen for the license plate recognition task and was fine-tuned on a small dataset of 534 images annotated by VOTT. This task was broken down into two main chunks, recognizing the license plate, and predicting the characters on the plate. These items are housed in a DIY 3D printed case designed by the hardware was assembled, focused on training an ML pipeline to process video from the Pi camera into license plate predictions. The hardware for the project includes a Raspberry Pi, Pi camera, 4G antenna, and a GPS antenna. The plate reader utilizes a combination of on-device and cloud computing to work in real-time. ![]() Robert Lucian Chiriac’s article describes a DIY device using ML to predict license plates in real-time.Įarlier this year Rober Lucian Chiriac wrote about creating a DIY device that can recognize and read license plates. ![]()
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