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指導教授 張元翔 報告人員 陳昱辰

An Approach to Korean License Plate Recognition Based on Vertical Edge Matching Mei Yu and Yong Deak Kim Ajou University Suwon, 442-749, Korea. 指導教授 張元翔 報告人員 陳昱辰. Introduction.

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指導教授 張元翔 報告人員 陳昱辰

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  1. An Approach to Korean License Plate Recognition Based on Vertical Edge MatchingMei Yu and Yong Deak KimAjou UniversitySuwon, 442-749, Korea 指導教授 張元翔 報告人員 陳昱辰

  2. Introduction • License plate recognition (LPR) has many applications in traffic monitoring systems. • Vehicle license plate recognition (LPR) is one form of automatic vehicle identification

  3. Korean License Plate Extraction • Edge Detection It is noticed that most of vehicles usually have more horizontal lines than vertical lines.

  4. Korean License Plate Extraction • Size-and-Shape Filtering Binary size-and-shape filter is very useful in pattern recognition, because it is usually needed to recognize objects with special shapes in images.

  5. For the binary image {E,,,}, the size-and-shape filter basedon seed filling algorithm is described as follows: • 1) Search the entire image row by row, for each white pixel E,,, in image, if it has not been checked, then run over the eight connected white region by using seed filling algorithm in which E,,, is adopted as the first starting seed of the region.

  6. 2) If it does not satisfy some predefined restricted conditions, then fill the region with black, that is,remove the region as noise, since it is impossible to be the region of interest (ROI) • 3) Continue to scan the image row by row to find another unchecked white pixel as the first starting seed of a new region, until all white pixels in the image have been checked.

  7. Korean License Plate Extraction • Edge Matching and License Plate Extraction The ratio of width to height of Korean license plate is about 2: 1, it can be used to judge whether two edge areas are the pair of vertical edges of a license plate. The vertical coordinates of the two vertical edges of a license plate should have small difference.

  8. Korean License Plate Extraction • after license plate is segmented, the percentage of character regions (white pixels) on a license plate is about from 10%to 40%. That is, if the percentage of character regions in the possible plate region is lower than 10% or higher than 40%,it can not be the real license plate region.

  9. License Plate Segmentation • Their backgrounds are green and yellow, while the characters are white and dark blue, respectively.

  10. License Plate Segmentation • Since luminance of different part of license plate may be not uniform because of the light condition, a license plate is separated into three or four parts when it is segmented. These parts are the part of region name and class code, the part of usage code, and the parts of serial number.

  11. Character Recognition • Template matching for character recognition is straightforward and can be reliable. Since characters on license plates have the same font, ternplate matching is employed for character recognition.

  12. Experiments and Analysis • the experiments are implemented in the following six aspects: (1) license plates in normal shapes (2) license plates that are out of shape or leaned due to the angle of view (3) license plates which have similar color to vehicle bodies, (4)damaged or bent license plates, (5) dirtylicense plates, (6) degraded images

  13. RESULT

  14. RESULT

  15. RESULT

  16. Conclusion • The proposed algorithm is fast enough, the recognition unit of a LPR system can be implemented only in software so that the cost of the system can be reduced.

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