A CNN-Based License Plate Recognition Using TensorFlow and PySpark

The use daily of vehicles is rising exponentially and as a result there is an increase in crimes associated with it. Many vehicles are violating the rules of traffic and so an abnormal number of accidents occur leading to a rise in the crime rates linearly. In order for any vehicle to be recognized, its license plate number is needed. Therefore, the vehicle license plate detection plays a notable role. The optical character recognition (OCR) is one effective way to scan number plates and recognize the text found in the digital image, containing the license plate number into machine readable text which can then be used to track the vehicles. The image of the number plate is first captured, processed, and every character present in the number plate is read for perfect recognition. The optical character recognition model is trained using TensorFlow. Spark's in-memory data engine can perform tasks rapidly in multi-stage jobs. Therefore, TensorFlow and Spark are used together to train and apply the OCR model to perform the license plate recognition swiftly.