License plate surveys are an important tool for transportation system planning for many years. These tools helps in origin-destination matrices, and provide basis for travel time estimations. The first generation of surveys involved human readers who would spend a lot of time in capturing data. The modern license plate surveys are based on video technology. License plate images are captured on videotape for data reduction at the analyst's office. If the tapes are manually transcribed, it's very expensive and labor intensive job.

This software developed for Indiana DOT was targeted to minimize the human role in the data reduction process and, in doing so, reduce the expenses involved. Image processing and artificial intelligence algorithms were implemented to extract license plate number and time stamp information from video data. A time stamp, that is present in all the frames, is also reported along with the license plate number. An open-source optical character recognition engine (OCR) was used to recognize individual characters in this software.

Video License Plate Data Reduction (VLPDR) software was developed and tested on a real dataset. The software can detect the presence of a vehicle and read the time stamp in approximately 85 percent of the cases, depending on the quality of the data based on lighting conditions, etc. Although the VLPDR software was not effective in recognizing most of the license plate numbers, VLPDR can reduced the labor time by upto 60 percent in manual transcription. The user is no longer required to work with difficult video players to identify and read frames with a vehicle present in the scene. The user only needs to use the software to identify and validate the results in a user friendly GUI.