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Hidden-Markov-Model-Based Segmentation Confidence Applied to Container Code Character Extraction
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DOI：10.1109/TITS.2011.2145417.png)
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Automatic container code recognition (ACCR) has become an indispensable aspect of current intelligent container management systems. In real applications, an ACCR module sometimes faces the problem of missing characters, i.e., not all the 11 container code characters (CCCs) appear in the input image. However, a few of the present methods can process container code images with missing characters. Therefore, a method is proposed to extract the CCCs for both the situation wherein all the 11 CCCs appear in an image and the situation wherein some CCCs are missing. In this method, hidden Markov model (HMM)-based segmentation confidence is proposed to describe the probability of the segmented characters belonging to the container code. Based on the segmentation confidence, the segmented characters are determined whether they belong to the container code or not, and if there are some characters missing, the positions of these characters can be estimated. Various container code images have been used to test the proposed method. The results of the tests show that the method is effective.
Keyword:
Character extraction
container code
hidden Markov models (HMMs)
image processing
segmentation confidence
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