EQ-LPR: EFFICIENT QUALITY-AWARE LICENSE PLATE RECOGNITION

Cong Zhang, Qi Wang∗, Xuelong Li

School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL),Northwestern Polytechnical University, Xi’an 710072, Shaanxi, P.R. China.

ABSTRACTLicense plate recognition (LPR) has attracted considerable at-tention due to its widespread applications in real life. Al-though numerous approaches based on image processing havebeen presented in the past few years, it is still an urgent is-sue to perform the LPR task efficiently in complex and un-constrained scenarios. To remedy this problem, an efficientquality-aware license plate recognition algorithm is proposedby introducing the siamese networks for plate stream recogni-tion and quality awareness in the traffic videos. Moreover, weexplore three progressive architectures for efficient and accu-rate recognition. Knowledge distillation is adopted to com-press the quality awareness network and make it lightweight.Extensive experiments have demonstrated the impressive per-formance and efficiency of the proposed method.

Index Terms— License Plate Recognition, Siamese Net-works, Quality Awareness, Knowledge Distillation

1. INTRODUCTIONRecently, license plate recognition (LPR) has received con-siderable research attention in the fields of image process-ing and computer vision. LPR system plays a vital role inthe maintenance of intelligent transportation systems (ITS),which has been widely utilized in numerous real-world ap-plications such as traffic law enforcement, access control,and automatic highway toll collections [1, 2, 3]. In gen-eral, license plates are specially designed for the vehicleidentification in traffic management systems, each of whichinvolves several diverse numbers and characters. An avail-able LPR system can accurately recognize each character ofthe localized license plates with various fonts, colors, andbackgrounds in different administrative regions. Therefore,considering these properties, it is reasonable to implementLPR systems based on image processing algorithms.

In the past few decades, considerable academic researcheson LPR have been published in the community, which canbe roughly divided into traditional methods [4, 5] and deeplearning-based methods [6, 7]. Thanks to the developmentof deep learning in object detection, the performance of li-cense plate detection has been dramatically improved yet

∗Corresponding author.This work was supported by the National Natural Science Foundation of

China under Grant U1864204, 61773316, U1801262, and 61871470.

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Frames in Real-Life Traffic Videos

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License Plate Stream

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Quality-Aware RecognitionSingle-Frame

Recognition Results

陕A1CQ11

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Update

Assign

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Fig. 1. There are six frames involved with the same licenseplate but at different times in a traffic video. The red charac-ters of the single-frame recognition results indicate incorrectrecognition that can be avoided by the proposed EQ-LPR.

robust and efficient plate recognition is still an urgent taskto be solved [8, 9, 10]. Regarding plate recognition, thereare two different approaches, i.e., segmentation-based meth-ods [7] and segmentation-free methods [6], where the lattergenerally consider plates as text sequences without charac-ter segmentation. Then they exploit convolutional neuralnetworks (CNNs) followed by recurrent neural networks(RNNs) to build the encoder-decoder model for sequencerecognition. In practice, license plate segmentation is reallya challenging task that is extremely sensitive to characterdistortion, occlusion, and illumination variation [6]. Conse-quently, segmentation-free plate recognition algorithms haveachieved better performance, attracting tremendous researchinterests in recent years [6, 11, 12].

These previous works contribute to the development ofLPR systems, some of which have been applied in severalexplicit conditions. However, there are the following signif-icant application limitations in these methods. (1) Most ex-isting LPR algorithms perform on individual images capturedby the sophisticated camera equipment in strictly specific en-vironments like parking lot entrances. Nevertheless, motionblur, defocus, and perspective distortion usually occur in real-life unconstrained traffic scenarios, e.g., intelligent driving,where many algorithms may not work well. (2) Despite con-siderable academic study on LPR systems, there are only afew video-based approaches to model temporal informationexplicitly [13]. Thanks to the development of video capturedevices such as dashboard cameras, it is convenient and low-cost to acquire real-life traffic videos that involve more valu-able information for LPR than single images. (3) Previous

existing video-based LPR techniques mainly focus on licenseplate super-resolution [14] and majority voting-based recog-nition [10], which introduce additional computational com-plexity and reduce efficiency. However, it is reasonable toevaluate each frame quality in license plate videos. As theprior knowledge, plate quality implies the degree of distor-tion that can be employed to guide recognition. As illustratedin Fig. 1, for a joint frame stream composed of the sameplate but at different times, clearer plate frames enable higherrecognition accuracy. Thus introducing plate quality estima-tion and temporal contextual information from videos mayimprove the robustness and effectiveness of the LPR systemespecially in complex unconstrained scenarios.

Against the above issues, an online efficient quality-awarelicense plate recognition (EQ-LPR) algorithm is proposed inthis paper. With a plate stream based on the tracklet, it firstautomatically evaluates the image quality of each plate frameand then recommends the recognition result of the currenthighest quality frame as the final decision. It is noteworthythat EQ-LPR works online without video future informationthat can be simply embedded as an efficient and robust recog-nition module in the real-life video-based systems.

The main contributions of this paper are as follows: (1)The siamese architecture is developed for quality-aware platerecognition, one subnetwork towards online plate streamquality awareness while the other for sequence recognitionbased on the selected frames by the former. (2) The qualityawareness network is further compressed for algorithm effi-ciency via knowledge distillation. (3) Extensive experimentshave presented the competitive performance of the proposedmethod and also demonstrate its effectiveness and efficiency.

2. METHODOLOGY

In this section, we first give the overview of the proposed al-gorithm and then introduce its implementation details.

2.1. Overview

Generally speaking, an LPR system available in real-life com-plex traffic scenarios can be divided into three steps or com-ponents: license plate detection, tracking, and recognition[13]. As mentioned above, there are several existing detec-tion and tracking approaches for video-based LPR systems,which have shown their impressive performance in uncon-strained and harsh environments [7, 10, 15, 16, 17]. How-ever, previous video-based methods usually ignore the impactof plate quality on recognition, some of which introduce ex-cessive temporal redundancy in neighboring frames, leadingto efficiency loss. To remedy these problems, we focus onefficient plate stream recognition where the potential plateregions have been favorably localized and linked with theirassigned identities by the well-trained detector and tracker,respectively. The lightweight quality awareness network isdeveloped for efficient stream quality estimation and plate

Algorithm 1 Efficient Quality-aware LPR (EQ-LPR)Input: The detected, linked and cropped license plate re-

gions with their respective identities from the videos, for-mulated by P = {P(j)

i : i ∈ [1, I], j ∈ [1, δ(i)]}1: Obtain the i-th plate stream Pi = {P(j)

i : j ∈ [1, δ(i)]}2: Set ai as the highest quality awareness score for Pi

3: Set r(j)i as the final recognition result for each P(j)i

4: for i = 1 to I do5: for j = 1 to δ(i) do6: if j = 1 then7: Produce the quality awareness score a(1)i

8: Produce the plate recognition results r(1)i

9: Initialize ai ← a(1)i , r

(j)i ← r

(1)i

10: else11: Produce the quality awareness score a(j)i

12: if a(j)i > ai then13: Produce the plate recognition results r(j)i

14: Update ai ← a(j)i , r

(j)i ← r

(j)i

15: else16: Assign r

(j)i ← r

(j−1)i

17: end if18: end if19: end for20: end for21: Get a = (a1, a2, · · · , aI)T , R = {r(j)i }Output: The final plate recognition results r(j)i for eachP(j)

i

frame recommendation. Moreover, motivated by [6, 12], weperform segmentation-free plate recognition controlled by thequality awareness network, which avoids segmentation errorscaused by distortion and blur in low-quality frames. For aclear illustration, denote the cropped plate regions as P whileI and δ(i) represent the maximum identity and the total framenumber of the plate stream with the current identity i, respec-tively. Then the process of EQ-LPR is summarized in Algo-rithm 1. To implement EQ-LPR, we explore three differentbut progressive frameworks depicted in Fig. 2, whose designpatterns and training strategies will be described in detail.

2.2. Siamese Networks for Quality-Aware LPR

As shown in Fig. 2 (a), the pseudo-siamese neural networkis first developed for quality-aware plate stream recogni-tion, named EQ-LPR-P. To be specific, there are two sep-arate networks without parameter sharing, one perceivingthe grayscale image quality of each frame in the stream andthe other recognizing the RGB plate image with the bestquality. These two networks have the same structure CNNmodels to extract deep discriminative features which will befed into their target-specific subnetworks for quality aware-ness and sequence recognition, respectively. However, thepseudo-siamese architecture EQ-LPR-P requires more com-

Max-Pool,

/2

Avg-Pool,

/2

Grayscale

Resize

Maxpool, /2

Conv, 16Conv, 32Conv, 32

Conv, 64Conv, 64

Maxpool, /2

Conv, 128Conv, 128

Maxpool, /2

Conv, 256Conv, 256

Maxpool, /2

Conv, 512

Maxpool, /2

Conv, 16Conv, 32Conv, 32

Conv, 64Conv, 64

Maxpool, /2

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Maxpool, /2

Conv, 512

Maxpool, /2

Attention

Conv, 512

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LSTM（Decoder)

Recognition

Results, 𝒓𝑖(𝑗)

(a) EQ-LPR-P

𝑁𝑟𝑁𝑞

FC Layer,ReLU

FC Layer,ReLU

QualityAwareness

Results, 𝑎𝑖(𝑗)

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Maxpool, /2

Conv, 512

Maxpool, /2

Conv, 64

Conv, 128

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Conv, 256

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Conv, 512

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Fig. 2. Overview of three different but progressive architectures for EQ-LPR, which involve the pseudo-siamese network,siamese network and compressed lightweight network respectively. All convolutional layers are configured with 3× 3 kernels.

puting resources without the shared CNN feature extractor.Therefore, we also introduce the common siamese networkto perform the proposed EQ-LPR, as depicted in Fig. 2 (b).Compared to EQ-LPR-P, EQ-LPR-S shares all parametersfor the two tasks during the feature extraction phase. Moreimportantly, both of EQ-LPR-P and EQ-LPR-S are developedin this work to explore two fundamental issues: (1) The platerecognition performance improvement by sharing discrimi-native features with plate quality estimation. (2) The modelcompression effectiveness of EQ-LPR-S for higher runningspeed and lower computational complexity. Formally, giventhe plate stream P(j)

i ∈ Pi with the same identity i, the deepfeature maps are generated by

P(j)i = G(P(j)

i ), F (j)q = Nq(P(j)

i ), F (j)r = Nr(P(j)

i ), (1)

where P(j)i represents the grayscale image of P(j)

i that hasbeen resized to the fixed size to avoid color interference.

As illustrated in Fig. 2, with the discriminative featuresencoded by the siamese network, F (j)

q will be processed bythe fully connected networkN ′q while F (j)

r is also further en-coded as the feature sequences by RNNs. In practice, theLSTMs are exploited as the encoder to alleviate gradient van-ishing or exploring. After that, the visual attention mecha-nism is introduced during the decoding phase which generatesrecognition results. This process can be formulated as

a(j)i = N ′q(F (j)

q ), r(j)i = N ′r(F (j)

r ), (2)

where a(j)i and r(j)i denote the produced quality awarenessscore and recognition results, respectively. Both EQ-LPR-Pand EQ-LPR-S can be automatically trained via the alternateand adaptive learning strategy. Concretely, the minimum soft-max prediction is regarded as the ground truth of a(j)i , i.e.,

α(j)i = min

k∈[1,K]

{softmax(y

(j)i,k )|y

(j)i,k ∈ {y

(j)i,1 , · · · , y

(j)i,K}

},

(3)

where K means the plate sequence length and y(j)i,k is the soft-max input for the k-th character prediction. With the recogni-tion labels θ(j)i , the loss functions can be formulated as

Lr = −∑K

k=1lnP (θ

(j)i,k |θ

(j)i,1:k−1), Lq = |α(j)

i −a(j)i |. (4)

It should be noted that the optimizations for Lr and Lq areperformed separately and alternately rather than joint trainingsince α(j)

i is generated based on the recognition results r(j)i .

2.3. Efficient Lightweight Quality Awareness NetworkBoth EQ-LPR-P and EQ-LPR-S can achieve quality-awareplate recognition based on the siamese architectures. How-ever, considering efficiency and real-life applications of LPR,we innovatively explore the model compression for the qual-ity awareness network, as shown in Fig.2 (c). Intuitively, thelightweight Nq can efficiently perform online plate qualityawareness in a short period of time. Furthermore, observingthat the inference of Nq is more frequent than the recognitionnetwork, it is reasonable to compress the quality awarenessmodule. Thus knowledge distillation [18] is exploited to com-press Nq into the lightweight N ∗q and F∗(j)q = N ∗q (P

(j)i ).

Specifically, based on EQ-LPR-S, we modify the loss func-tion Lq to L∗q by introducing additional constraints, given by

L∗q = |α(j)i − a

(j)i |+ ‖F

∗(j)q −F (j)

r ‖2, (5)

where `2 norm is employed to minimize the element-wise dis-tance betweenF∗(j)q andF (j)

r . In the formulation, F∗(j)q aimsto fit the feature representation inF (j)

r via supervised learningand the training strategy follows the description above.

3. EXPERIMENTS

Extensive experiments are conducted in this section to evalu-ate the performance of the proposed algorithm. Moreover, wepresent and analyze its effectiveness and efficiency by com-parison with other methods based on the quantitative results.

The Range of Quality Awareness Scores

Num

ber o

f Lic

ense

Pla

te F

ram

es

Fig. 3. The distribution of quality awareness scores in twodifferent datasets, which are normalized to the range of [0, 1].

ResultsLicense Plate Streams and Quality Awareness Scores

AVD4791

MAW3186

0.99 0.99 0.78 0.180.26

0.39 0.83 0.91 0.090.98

0.99 0.48 0.94 0.560.99

沪B75177

Fig. 4. Exemplar recognition results of the proposed algo-rithm EQ-LPR-E on different datasets and the red values in-dicate the final awareness scores of the plate streams.

3.1. Datasets and Implementation DetailsTo comprehensively evaluate the proposed LPR algorithm,two different datasets are exploited in the experiments. Thefirst one is UFPR-ALPR dataset released in [10], which con-sists of 4, 500 license plate images captured inside the drivingvehicles. All images in this dataset are obtained from 150real-life traffic videos, corresponding to 150 license platesand each plate involving 30 frames at different times. More-over, they are acquired with three different cameras, whichensures the diversity of plate images. In practice, for the faircompetition, we follow the protocol division proposed in [10],i.e., 60% for training and 40% for testing. Note that data aug-mentation is adopted in both the training and testing phases toavoid overfitting and expand the dataset. The second dataset,namely NWPU-LPR, is composed of 8, 500 images after dataaugmentation, corresponding to 1, 700 different plates andeach plate involving 5 frames. With various distortions likenoise, these images are acquired in harsh environments in dif-ferent provinces of China. In the evaluation, 6, 000 imagesare utilized for training and others for testing.

3.2. Performance Evaluation and Effectiveness AnalysisExtensive experiments are conducted to evaluate the perfor-mance of the proposed three different architectures. Note thatonly accurately predicting the entire plate sequence means thecorrect recognition. As shown in Table 1, the proposed ar-chitectures outperform these existing methods on the UFPR-ALPR dataset. Furthermore, the third architecture EQ-LPR-

Table 1. Recognition performance and speed comparison ofdifferent methods on UFPR-ALPR dataset.

Methods Recognition Accuracy (%) Time (ms)Sighthound [19] 47.39 –OpenALPR [20] 50.94 –Laroca et. al [10] 78.33 28

EQ-LPR-P (Ours) 88.58 96EQ-LPR-S (Ours) 90.31 66EQ-LPR-E (Ours) 90.14 43

Table 2. Recognition performance and speed comparison ofdifferent methods on NWPU-LPR dataset.

MethodsRecognition Accuracy (%) Overall Time

Per Frame(ms)

ExcludingChinese Characters

IncludingChinese Characters

EasyPR [21] 77.14 70.60 –BaiduLPR [22] 92.33 87.25 –

EQ-LPR-P (Ours) 93.09 91.21 106EQ-LPR-S (Ours) 96.07 93.86 81EQ-LPR-E (Ours) 95.27 92.91 49

E takes only 43 ms per frame (23.26 fps) yet its recogni-tion accuracy is 11.81% higher than the method in [10] withonly additional 15 ms. It makes sense even for some real-time applications. However, since most images in the UFPR-ALPR dataset are captured in the driving scenarios with slowspeeds, we specifically build the NWPU-LPR dataset to betterdemonstrate the effectiveness of the proposed efficient qual-ity awareness network. In this dataset, image distortions suchas motion blur and illumination variance usually occur, mak-ing it more challenging than the first one. As illustrated inFig. 3, poor-quality images occupy a higher proportion in thedataset, leading to difficult recognition tasks. For the NWPU-LPR dataset, we take two public APIs toward Chinese LPR ascomparison methods [21, 22] and our algorithms also achievebetter recognition accuracy in Table 2. Concretely, all archi-tectures of EQ-LPR outperform the comparison methods inrecognition accuracy, which demonstrates the effectiveness ofthe quality awareness network for video-based LPR systems.Moreover, the lightweight EQ-LPR-E architecture shows itsefficiency and speed benefits with the acceptable reductionin recognition performance, which is of constructive value tothe real-life applications. The exemplar recognition results onthe two different datasets are exhibited in Figure. 4, the firsttwo rows and the last one corresponding to the UFPR-ALPRdataset and NWPU-LPR dataset, respectively.

4. CONCLUSIONS

In this work, an efficient quality-aware license plate recog-nition algorithm is proposed for online and fast LPR sys-tems especially in complex scenarios, which integrates qual-ity awareness and plate sequence recognition into one net-work. Moreover, it involves three different but progressivearchitectures based on the siamese networks or knowledgedistillation. Competitive experimental results on two datasetsdemonstrate the efficiency and effectiveness of EQ-LPR.

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