OCR algorithms work better with high quality images for accurate Automated License Plate Recognition ALPR

Unlike what is shown on TV, you cannot zoom into a blurry image and expect to get more details. An image with acceptable sharpness and contrast must be acquired with the appropriate system from the start. This means the right image sensor, camera, optics, and lighting all combined in a reliable way.

So what defines good image quality for ALPR?

The first step is to have reliable triggering in order to have the license plate in the proper location in the image, which can be especially difficult in multi-lane systems. After that, a good/accurate image can be described by:

Good Sharpness
Sufficient Contrast
Free of artifacts
And sometimes with accurate color

These are qualitative explanations so here some images to demonstrate the point.

OCR image for ALPR

Figure 1. Artifacts from insufficient lighting control

ITS ALPR Motion Blur

Figure 2. Insufficient Sharpness due to Motion Blur

Low contrast ITS ALPR

Figure 3. Insufficient Contrast from limited dynamic range

Some good background information on license plate acquisition algorithms and technology is provided on: http://www.platerecognition.info/1102.htm

The sources of these image quality issues can vary. Some possible reasons are shown in the table below and are further detailed in our next series of blogs.

Image Quality Parameter	Corresponding Source of Limitations	Image System Parameters to Control
Sharpness	Limited Depth of Field Motion Blur Variable Lighting	F Value of Lens Sensitivity of Image Sensor Iris Control
Contrast	Limited number of images Reflections of the license plate Reflections of snow, rain, flog	Frame rate of the image sensor/camera Dynamic range of the image sensor/camera
Artifacts	Ghost images Bright spots and streaks from sun exposure and reflections	Alignment of filter, lens, and lighting Channel Matching in the camera Blooming and smear control in the camera
Color	Inaccurate Color reproduction	Accurate color calculations and automatic white balance in the camera

Proper alignment of the entire optical path determines the quality of the image captured, which is especially vital in high-speed situations. With a higher quality of the input image, the better starting point for the license plate recognition algorithm, and therefore the higher license plate recognition accuracy.

For more details on optimizing sharpness and contrast, and minimizing artifacts, see our blogs in the coming weeks.

Can’t wait?

You can already read the following blogs about this: