Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection

Table 3

Performance comparison of some typical ALPR systems for LPD.

Methods	Main procedures for license plate detection	Database size	Image conditions	LPD Rate	Processing time	Real time	Plate format
[10]	Sliding concentric windows, histogram	40 images	640 × 480 pixels (Different distances and weather, road)	82.5%	—	—	Korean plates
[11]	Vertical edge, edge filtering, and morphological operation	350 images	Different distances and weather and road	95.2%	—	—	Iranian plates
[12]	Vertical edge detection, unwanted line elimination	664 images	640 × 480 pixels (various weather conditions, road)	91.65%	47.7 ms	Yes	Malaysian plates
[13]	Scan line, texture properties, color, and Hough transform	332 images	867 × 623 pixels (various illumination and different distances and road)	97.1%	0.53 s	No	Taiwanese plates
Our proposed method	Cascade AdaBoost algorithm and adaptive thresholding	1800 images	1280 × 720 pixels, various weather conditions and different illumination	98.38%	49 ms	Yes	Korean Plates