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Time series analysis for enhancing the recognition of license plate number in video stream of IOT camera

License plate recognition is the most promising application in IoT camera. The existing recognition algorithm shows good performance when it is used in the constrained situation such as at the entrance to the parking lot. The number plate images taken from the cameras of the parking lot system have a constant size and brightness. The accuracy of the license plate recognition system installed at the entrance to the parking lot is more than 98%. However, the recognition accuracy of license plate number may drop by about 50% to 70% in video stream taken on roads, where the size and the brightness of number plate images in video stream are not constant. They are very different to each other due to the camera angle, the distance to the vehicles and the light. This situation makes some objects to be recognized as license plates compared to the constrained situation. A major cause of this low recognition accuracy is the inability to filter out noise like an outlier that has a similar pattern to license plates. More deeper and wider CNN technique can be useful to increase the accuracy in these situation. But, the IoT camera generally has a restricted resource and is unable to use too large deep CNN, which requires a lot of computation power and memory. This paper suppresses outliers, non-license plate objects and increases the recognition accuracy through time series analysis. We use the time series analysis as the statistical methods to solve this problem of the outlier occurrence. Experimental results show that the recognition accuracy in video stream taken on ordinary roads is 77% in the case of the absence of outlier filters, but 85% in the case of including the filters proposed in the paper.