FEATURED TOPIC

Automatic License Plate Detection and Recognitionin Thailand
Yasuo OGIUCHI*, Masakatsu HIGASHIKUBO, Sakda PANWAI and Ekarin LUENAGVILAI

A license plate recognition (LPR) system with image sensors, which enables vehicle identification, plays a very
important role in traffic flow control. In Thailand, chronic traffic congestion due to rapid motorization is a serious
problem, and LPR can be one of the solutions. However, there are some technical obstacles to overcome such as therecognition of the Thai alphabet. We have developed a novel method that enables the LPR system to work wellregardless of the plate format or alphabet. We conducted experiments using image data collected in Thailand, andobtained positive results with a plate detection rate of 94.6% and a character recognition rate of 92.0%. We willcontinue to improve the performance of the LPR system for practical use in Thailand.
Keywords: license plate, license plate detection, character recognition, Thailand, pattern recognition

1. Introduction
In recent years, chronic traffic congestion due to rapid
urbanization and motorization is a serious problem inSouth East Asia including Thailand. Adequate traffic flowcontrol, based on information collected with various kindsof sensors, like image sensors, is one of the solutions.
Among many ways of collecting traffic information, license plate recognition (LPR) with image sensors is very
useful for travel time measurement because it enables vehicle identification without a special device. Therefore,
many methods for license plate detection and recognition
have been proposed. However, they tend to assume a specific format or use of Latin alphabet or Japanese characters, and are difficult to apply to license plates in many
Asian countries like Thailand, where local alphabet is usedon license plates.
We have developed a novel method for detection andrecognition of various types of license plates(1). The method
assumes neither a specific format nor a specific charactersystem. In this paper, we examined the method with collected images of license plates in Thailand.

2. Methods
2-1 Outline of license plate in Thailand
Examples of license plates in Thailand are shown in
Photo 1.
Outline of license plates in Thailand are as follows(2):General license plates
License plates for general cars are 15 by 34 centimetersin size, with a colored and embossed outline. The registration ID consists of two series of Thai letters followed by aserial number string up to four digits, from 1 to 9999. The
province of registration is displayed in Thai under the registration ID. The colors of the text and the background differ depending on the type of vehicle.
Bus and truck plates
License plates for buses and trucks are 22 by 44 cen-

Photo 1. Examples of license plates in Thailand

timeters. The registration ID consists of two digits and four
serial digits, separated by a horizontal line. The word
“THAILAND” is displayed above the ID, and the provinceof registration is displayed below the ID. The color of thetext is black, and the color of the background is yellow orwhite.
2-2 Outline of the methods
Figure 1 shows the outline of the proposed method.
First, plate detection process is performed on the input
image, and character recognition process is performed on
the detected license plate. These two processes are independent. The targets of character recognition were vehicle
ID only.
2-3 License plate detection
Figure 2 shows the outline of plate detection.
Bag of Features(3) based on Histogram of Oriented
Gradient (HOG-based BoF) and linear support vector machine (SVM) were adopted for a detector. Robustness
against illumination or environmental change can beachieved with HOG, and robustness against various typesof fonts can be achieved with BoF.
At the training phase, the study constructed a detector
with a lot of positive and negative samples. These samples
were selected randomly from the collected images.

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Fig. 3. Outline of character recognition. HOG feature space is described
as a 2-dimentional plane for simplicity of explanation.

3. Experiments

Fig. 1. Outline of the proposed system

Fig. 2. Outline of detector training and detection

2-4 Character recognition
Figure 3 shows the outline of character recognition.
HOG-feature was adopted for character recognition.
At the training phase, we adopted generative training(4). Training data for character recognition was generated from a high-resolution image of each letters (44 Thailetters and 10 Arabic numbers), and distribution of eachletter in the HOG-feature space was then obtained.
At the recognition phase, each character is cut out
from the image, calculated the HOG-feature vector, andrecognized characters with the distribution in HOG-feature
space obtained above.

40 · Automatic License Plate Detection and Recognition in Thailand

3-1 Image data for the experimentImage data sets used in the experiment were collectedon the Expressway in Bangkok, Thailand. Examples of thecollected images are shown in Photo 2.
Among these collected images, 18,674 images were selected at random. For images with license plate, size, position, and registration ID of the plate were recorded byhand.
In the experiment, license plates in the images weredetected with the plate detector. If the tested image has nolicense plate, the right answer is “NO PLATE.”
After that, the registration ID on the detected license
plate was recognized. As mentioned above, province nameson the plates were not the target of this experiment.
3-2 Training
HOG-based BoF is used as a feature, and a support vector machine as a detector. As mentioned above, 3,000 images of license plate and 1,500 images of other parts of carswere randomly selected as a training data set.
The training data set for character recognition wasgenerated from a high-resolution image of each letter (44Thai letters and 10 Arabic numbers), considering the difference of fonts and conditions of dirt or damages. Although the fonts of Arabic numbers on general license
plates and those on the bus and truck plates are different,
training data were common in the experiment.
3-3 Detection and recognition
10,174 images with license plate and 4,000 images withno license plate were randomly selected as evaluation data.
First, license plates in the images were detected. Afterthat character recognition was performed when the license
plate was successfully detected.

4. Results
Terms about the results of the experiments are defined as follows:• Success of detection: License plate is successfully detected for image with license plate.

Photo 2. Examples of collected images• Miss: License plate is not detected for image with license
plate.
• False Alarm: Some parts other than license plate are detected as “License Plate.”• Success of Thai alphabet recognition: First part of theregistration ID (Normally 2 Thai letters or 2 Arabicnumbers) is recognized successfully.
• Success of Arabic number recognition: Latter part of theregistration ID is recognized successfully.
• Success of all character recognition: All characters of theregistration ID is recognized successfully.
Table 1 shows the results of license plate detection andcharacter recognition. 210 of 4,000 images with no license
plate had one or more false positives. Average number offalse positives per image (with or without plate) was about
4.0.
Examples of the results of license plate detection are
shown in Photos 3 to 5.
Photo 3 shows examples of successful detection.
Photo 4 shows examples of miss for images with license
plate. Types of misses are detection failure of license platesof low luminance or plates with red background in thenighttime, or detection failure of license plates with surface
damage or dirt.
Photo 5 shows examples of false positive for images
without a license plate. All of false positives on images with

Photo 3. Examples of success of detection

Table 1. Results of license plate detection and recognition

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Photo 4. Examples of misses
Fig. 4. Examples of the result of character recognition.
The results with asterisk (*) are recognition failure,and characters with under bar are misrecognized.

Photo 5. Examples of false alarm for images without license plate

no license plate were the detection of a character on nonlicense-plate objects.
Examples of the results of character recognition areshown in Fig. 4.
The processing time per image was about 300 ms (implemented on Windows 7 Pro 32 bit / Intel Core i-7 880 /C language).

ter-like curves. Therefore, it is essential to measure false
alarms.
Character recognition based on feature vector distribution on HOG feature space enables flexible recognition,e.g. handles both fonts for general license plates and forbus and truck plates, and is valid for the Thai alphabet andArabic numbers. Most of failures of recognition werecaused by dirt or damage of license plates, and not by thefault of the method itself.
Our future works are as follows:(1) Improvement of performance
At this time, the rate of detection or character recognition and the number of the false positives are not at a
sufficient level. The performance must be improved for all
the license plates including dirty or damaged plates.
(2) Computation cost
At this time, our method needs much computation
cost. In order to transplant the LPR method from PC to aroadside device, the method needs to be optimized to fit
the road-side device performance.
(3) Performance under extreme conditions
For practical use in Thailand, or other tropical countries, the study should cover more images under extreme
conditions, like under heavy squalls.

5. Discussion
6. Conclusion
The results show that:
The combination of license plate detection with HOGbased BoF and character recognition with distribution in
HOG-feature space is valid for license plates in Thailand,
but the false positive rate is still high for practical use. Ourmethod detects a license plate in the image as a “set ofcharacter-like curves.” Therefore, it can handle license
plates regardless of their formats. However, as a side effect,
it mistakenly detects non-license-plate objects with charac-

42 · Automatic License Plate Detection and Recognition in Thailand

This paper proposed a novel method for detection
and recognition of license plates in Thailand. We performed a set of experiments and the results showed thatthe methods of license plate detection and character recognition are valid for license plates in Thailand. Performance
improvement and evaluation of performance under extreme conditions are our future work.

7. Acknowledgements
Special thanks to the Expressway Authority of Thailand (EXAT) for providing valuable information and facilitating convenience during data collection in Thailand.

Contributors (The lead author is indicated by an asterisk (*).)

Y. OGIUCHI*• Ph.D.
Assistant Manager, Infocommunicationand Social Systems R&D Center· Intel and Intel Core are trademarks or registered trademarks of Intel Corporation in the U.S. and/or other countries.

M. HIGASHIKUBO• Group Manager, Infocommunicationand Social Systems R&D Center

Technical Terms*1 Histogram of Oriented Gradients (HOG): HOG is animage feature value. An image is divided into multiple
local regions, and a side-by-side vector of histograms
oriented in the direction of the luminosity gradient ofeach region is calculated as the image’s feature value.
One characteristic of this method is that it is robustwith regard to differences in position and variations inillumination.
*2 Bag of Features (BoF): BoF is a feature value which describes a set of local features as a single histogram. Itis very simple description, with information aboutplaces abstracted. Recently, it is used widely for object
recognition.
3* Support Vector Machine (SVM): An SVM is a methodof pattern recognition. In theory, it is the most efficient method for solving two-category classification
problems and provides high identification performance when dealing with unlearned data.

(1)

(2)

(3)

(4)

S. PANWAI• Ph.D.
Expressway Authority of Thailand

E. LUENAGVILAI• Expressway Authority of Thailand

References
Yasuo Ogiuchi, Masakatsu Higashikubo, Sakda Panwai, Ekarin
Luenagvilai. “Automatic Detection and Recognition of License Platesin Thailand,” In Proceedings of 20th ITS World Congress (Tokyo,October 14-18, 2013)“Vehicle registration plates of Thailand - Wikipedia, the free encyclopedia.” Available at
http://en.wikipedia.org/wiki/Vehicle_registration_plates_of_Thailand(accessed on July 27, 2013)
Nowak Eric, Frédéric Jurie, and Bill Triggs. “Sampling strategies forbag-of-features image classification.” in Proceedings of 9th European
Conference on Computer Vision (Graz, Austria, May 7-13, 2006)
Ishida Hiroyuki, Takahashi Tomokazu, Ide Ichiro, Mekada Yoshito,
and Murase Hiroshi. “Identification of degraded traffic sign symbols
by a generative learning method.” In Proceedings of 18th International Conference on Pattern Recognition (Hong Kong, August20-24, 2006)

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