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Morphology Based Thresholding for Character Extraction
Yasuko TAKAHASHI Akio SHIO Kenichiro ISHII
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1993/10/25
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)
image processing, mathematical morphology, character extraction, edge detection, machine vision, thresholding,
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The character binarization method MTC is developed for enhancing the recognition of characters in general outdoor images. Such recognition is traditionally difficult because of the influence of illumination changes, especially strong shadow, and also changes in character, such as apparent character sizes. One way to overcome such difficulties is to restrict objects to be processed by using strong hypotheses, such as type of object, object orientation and distance. Several systems for automatic license plate reading are being developed using such strong hypotheses. However. their strong assumptions limit their applications and complicate the extension of the systems. The MTC method assumes the most reasonable hypotheses possible for characters: they occupy plane areas, consist of narrow lines, and external shadow is considerably larger than character lines. The first step is to eliminate the effect of local brightness changes by enhancing feature including characters. This is achieved by applying mathematical morphology by using a logarithmic function. The enhanced gray-scale image is then binarized. Accurate binarization is achieved because local thresholds are determined from the edges detected in the image. The MTC method yields stable binary results under illumination changes, and, consequently, ensures high character reading rates. This is confirmed with a large number of images collected under a wide variety of weather conditions. It is also shown experimentally that MTC permits stable recognition rate even if the characters vary in size.