License plate recognition application using extreme learning machines

Recording a car license plate is an important task for police officers or security officers to check the car of interest. However, manually recording these plates comes with problems. It is easy to make a mistake, or it can be lost. The Extreme Learning Machine (ELM) can classify the plates faster and it is a more accurate system. Therefore, this paper proposes a new license plate recognition system using ELM. The proposed system is composed of two parts: the first is a mobile application to take a picture of the car license plate, and the second is the recognition system using ELM. The recognition system entails two parts: the first is to preprocess and extract features using the histogram of oriented gradients (HOG). The second part is to classify each number and each of the Thai alphabet letters that appear on the car license plates. Also, the system will classify provinces of each plate. The results of the experiment show that the testing recognition rate when trained with 200 hidden nodes is 89.05% while the rate of correctly recognized plates is 252 out of 283 plates.