This week, I worked on the proposal presentation slides, conducted background research on license plate recognition methods, and explored available recognition models like OpenALPR, EasyOCR, and YOLO. I also examined competitors, including Genetec, PLATESMART MOBILE DEFENDER, and Nvidia Metropolis. I experimented with online available solutions and found that current methods usually involve several steps of narrowing down the image to the license plate before running OCR. For example, they would locate the car in the image, then the license plate, and then run the character recognition. In my research, I also discovered that OpenALPR, although free, has not been updated in 5-7 years and seems to have relatively poor performance compared to more modern alternatives.
My progress is on schedule, and next week I plan to work on the design proposal, research available and relevant datasets, and try the baseline yolov11 without fine tuning to see if license plates were already one of the classes in the training set and how it performs. I will also research different preprocessing techniques to improve recognition accuracy under varying conditions such as lighting and motion blur.