Vision-based identification extraction for freight containers

The car license plates have regular formats and styles, and can be recognized by computer vision systems. However, some information that exclude from the alphanumerical characters of the car license plates are printed directly on a car body. Unlike car license plates, freight container's identifications have different colors and alignments. Some car bodies are not flat, such as trucks and freight containers, which increases the difficulty for character recognition. The character intervals in the identifications are varied from each other, and other information such as advertisement and logos are also marked on the car body, which make it hard to recognize characters too. Based on computer vision techniques, a fast and efficient method to extract the identification from freight containers is presented in this paper. The identification formats and styles conform to the standard of ISO/TC104. The information fliat does not pertain to part of the identification is eliminated during the processing. The most possible groups remaining in the image are extracted for identifications. Finally, an optical character recognition algorithm, Tesseract, is applied to recognize character images and then converted into texts. Thirty-five back surface images of freight containers are used to verify the effectiveness of the proposed method. The resolution of the images is set to 600 × 500. From the experimental results, we are encouraged that the accurate rate of identification extraction is over 90.37% and the average execution time is 12.91 seconds achieved by our proposed method.