Blog_banner_beyond_the_datasheet

Best Image for Automatic License Plate Recognition – ALPR – part 3 Remove Image Artifacts

Posted by Gretchen Alper on Thu, Mar 15, 2012

Usable images for ALPR are sharp, contrast rich and without artifacts. Reducing image sensor artifacts is probably the most difficult thing to do, but here are some artifacts that camera manufacturers can help to remove or minimize that are specific to the needs of ALPR:

Preventing Ghost Images

Ghost images can appear if Infrared (IR) lighting is used in combination with a visible light block filter (See Figure 1).  By using the correct filters, ghost images can be decreased as long as the filter is properly aligned with the lens, camera, and the lighting.  The simplest way to prevent ghost images and lens artifacts from interfering with the system performance is to utilize a camera supplier that also has the expertise to properly integrate the filter and lens with the camera.

ghosting alpr traffic camera system

Figure 1:  Example of ghost images (one digit of the license plate has been obscured for privacy)

Manage Blooming and Smear

Blooming and smear are challenges with outdoor vision systems, where blooming and smear (streaks) are artifacts created by saturation from very bright spots in a scene (See Figure 2).  Bright spots can originate from headlights, reflections off license plates, the sun at certain times of the year, or sun reflecting on the road.  Image processing in the system cannot correct these artifacts so blooming and smear must be managed in the camera through special functionality to ensure that the license plate is not obscured in the original image data.

describe the image

Figure 2.  Example of Blooming and Smear

 

Improve Channel Matching

Even with effective management of blooming and smear, direct sunlight can cause a poor image if the image sensor channel matching is insufficient in the camera. Image sensors usually have 2 or 4 readout channels that need to be stitched together in the camera to recreate the complete image. Cameras with bad channel matching can deliver images with one part overexposed and the other part underexposed. This leads to poor performance of the OCR algorithm.

Since automatic license plate recognition (ALPR) or automatic number plate recognition (ANPR relies on optical character recognition (OCR) of images, it makes sense that a higher quality input image results in higher accuracy.   

  

With the proper alignment of the lens, filter, camera, and lighting, as well as specialized functionality in the camera to deal with extreme lighting conditions of traffic applications, image artifacts are reduced or eliminated.  This combined with optimized sharpness and contrast, results in high quality images and therefore better license plate recognition rates.


Topics: Applications

Previous blog:

Next blog: