Abstract
This paper proposes a license plate recognition algorithm that consists of three major steps: image preprocessing, segmentation, and recognition, which works efficiently with day- and nighttime images, as well as with the license plate being tilted. Pre-filtration allows the sequential binarization to be conducted efficiently. Typically, the license plate segmentation is realized by a histogram method with the preliminary plate de-rotation to the horizontal position, thus deteriorating the original image quality. In this paper the segmentation is implemented by a connected components method, enabling the rotation and a consequent loss of quality to be avoided. The hierarchical temporal network shows good results in rotated symbols recognition. The proposed method can be used in a similar way for segmentation and recognition of various text data. The proposed algorithms can also be used for distorted text segmentation and recognition.
| Original language | English |
|---|---|
| Pages (from-to) | 275-280 |
| Number of pages | 6 |
| Journal | Computer Optics |
| Volume | 39 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Apr 2015 |
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Keywords
- Hierarchical temporal memory
- License plate detection
- Temporal grouping
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Computer Science Applications
- Electrical and Electronic Engineering
Cite this
License Plate Recognition Algorithm on the basis of a Connected Components method and a Hierarchical Temporal Memory Model. / Bolotova, Y. U.; Spitsyn, V. G.; Rudometkina, M. N.
In: Computer Optics, Vol. 39, No. 2, 01.04.2015, p. 275-280.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - License Plate Recognition Algorithm on the basis of a Connected Components method and a Hierarchical Temporal Memory Model
AU - Bolotova, Y. U.
AU - Spitsyn, V. G.
AU - Rudometkina, M. N.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - This paper proposes a license plate recognition algorithm that consists of three major steps: image preprocessing, segmentation, and recognition, which works efficiently with day- and nighttime images, as well as with the license plate being tilted. Pre-filtration allows the sequential binarization to be conducted efficiently. Typically, the license plate segmentation is realized by a histogram method with the preliminary plate de-rotation to the horizontal position, thus deteriorating the original image quality. In this paper the segmentation is implemented by a connected components method, enabling the rotation and a consequent loss of quality to be avoided. The hierarchical temporal network shows good results in rotated symbols recognition. The proposed method can be used in a similar way for segmentation and recognition of various text data. The proposed algorithms can also be used for distorted text segmentation and recognition.
AB - This paper proposes a license plate recognition algorithm that consists of three major steps: image preprocessing, segmentation, and recognition, which works efficiently with day- and nighttime images, as well as with the license plate being tilted. Pre-filtration allows the sequential binarization to be conducted efficiently. Typically, the license plate segmentation is realized by a histogram method with the preliminary plate de-rotation to the horizontal position, thus deteriorating the original image quality. In this paper the segmentation is implemented by a connected components method, enabling the rotation and a consequent loss of quality to be avoided. The hierarchical temporal network shows good results in rotated symbols recognition. The proposed method can be used in a similar way for segmentation and recognition of various text data. The proposed algorithms can also be used for distorted text segmentation and recognition.
KW - Hierarchical temporal memory
KW - License plate detection
KW - Temporal grouping
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U2 - 10.18287/0134-2452-2015-39-2-275-280
DO - 10.18287/0134-2452-2015-39-2-275-280
M3 - Article
VL - 39
SP - 275
EP - 280
JO - Computer Optics
JF - Computer Optics
SN - 0134-2452
IS - 2
ER -