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# LICENSE PLATE RECOGNITION BASED ON MULTI-ANGLE VIEW MODEL

Dat Tran-Anh1, Khanh Linh Tran2, Hoai-Nam Vu2

Tác giả liên hệ: Vũ Hoài Nam, email<EMAIL_ADDRESS>1Thuyloi University
2Posts and Telecommunications Institute of Technology

###### Abstract

In the realm of research, the detection/recognition of text within
images/videos captured by cameras constitutes a highly challenging problem for
researchers. Despite certain advancements achieving high accuracy, current
methods still require substantial improvements to be applicable in practical
scenarios. Diverging from text detection in images/videos, this paper
addresses the issue of text detection within license plates by amalgamating
multiple frames of distinct perspectives. For each viewpoint, the proposed
method extracts descriptive features characterizing the text components of the
license plate, specifically corner points and area. Concretely, we present
three viewpoints: view-1, view-2, and view-3, to identify the nearest
neighboring components facilitating the restoration of text components from
the same license plate line based on estimations of similarity levels and
distance metrics. Subsequently, we employ the CnOCR method for text
recognition within license plates. Experimental results on the self-collected
dataset (PTITPlates), comprising pairs of images in various scenarios, and the
publicly available Stanford Cars Dataset, demonstrate the superiority of the
proposed method over existing approaches.

###### Index Terms:

deep learning, license plate recognition and detection.

##  I INTRODUCTION

In recent decades, the traffic situation has become significantly more complex
due to the global population increase [1]. Intelligent Transportation Systems
(ITS) have been developed as a solution to the global traffic issue. To deploy
ITS models, the management and automated recognition of vehicle license plates
are considered crucial components. Figure 1 illustrates the basic flow of
license plate recognition software. License Plate Recognition (LPR) using
smart camera systems typically involves four steps: firstly, the conversion of
camera images into a format suitable for computer processing; next, the
identification of regions of interest within the monitored camera image;
subsequently, the recognition of characters on the license plate; finally, the
presentation of the license plate recognition results [2].

Figure 1: Image processing pipeline for License Plate Recognition.

The traditional approach views a vehicle license plate as a region of interest
and recognizes the characters as a sequence, followed by comparing two
sequences to identify the vehicle. In the research study by Vaishnav et al.
[3], the authors proposed a system that utilizes optical character recognition
techniques and compares characters with stored templates to obtain specific
information about the license plate. Comparing license plate numbers yields
accurate results; however, this method is effective only when the license
plate is clearly displayed in the image. If the license plate is obscured or
not securely attached to the registered vehicle, inaccurate results may be
obtained.

These issues can be addressed by utilizing additional features of the license
plate for comparison. Unlike the traditional approach, deep learning models
based on multi-layered architectures can learn license plate characteristics
at different levels. These deep learning models take raw images (without
feature extraction) as direct input. Most deep learning methods for license
plate recognition learn the plate features within the model [14, 5].
Kessentini et al. [6] designed a two-stream architecture: (i) stream 1
processes input vehicle features; (ii) stream 2 processes input license plate
features. However, this method only performs license plate recognition from a
single viewing angle. Recently, with the rapid development of widely
distributed camera systems, multi-angle data collection has become feasible.
Consequently, license plate recognition systems can benefit from this multi-
angle data. Different viewing angles of the license plate provide the
opportunity to extract diverse features, which are useful for recognition. In
this study, we apply the YOLOv8 architecture, take license plates from
multiple viewing angles as input, and propose a deep learning model for
accurate license plate recognition in various real-world situations.

##  II RELATED WORK

License plate recognition is divided into two main stages: (1) the license
plate detection stage and (2) the license plate recognition stage.

###  II-A License plate detection stage.

Recently, computer vision-based methods for license plate detection have
garnered significant attention in Intelligent Transportation System (ITS)
applications. Achieving highly accurate license plate detection is a
fundamental component of traffic monitoring aimed at increasing safety and
automation [7]. A comprehensive survey evaluating license plate detection
systems is presented in [8]. With the recent strong advancements of deep
learning algorithms in various image processing and pattern recognition
domains [9, 10, 11, 12], single-camera object detection systems based on
Convolutional Neural Networks (CNNs) have been investigated [13, 14]. However,
these single-camera systems might not be able to detect partially obscured
license plates in congested traffic contexts. An alternative approach to
overcome this challenge is to employ multi-camera systems and integrate
information from each independent camera stream [15, 16]. Mukhija et al. [17]
proposed a method based on wavelet transform and Empirical Mode Decomposition
(EMD) for license plate localization in images, addressing real-world
challenges such as lighting variations, complex backgrounds, and changes in
surroundings. MASK-RCNN [18] introduced a simplified, flexible, and popular
segmentation framework that can create masks for potential objects and
accurately segment targets. The YOLO model and its upgraded versions [19]
consider object detection as a regression task, enabling efficient object
detection with high accuracy and fast speed. Deep learning models and
architectures based on YOLO are increasingly popular in the research
community. Therefore, YOLOv8 is utilized in this paper as the framework for
the license plate detection component in our system.

###  II-B License plate recognition stage.

Some license plate recognition systems are designed to segment characters
before recognizing them. Segmentation methods can be categorized into
connected-component analysis [20], projection profile analysis [21], prior
character knowledge [22], contour analysis around characters [23], and
combinations of these methods [24]. It becomes evident that accurately
classifying all characters within a license plate is challenging when the
character segmentation component performs poorly. Consequently, some
researchers focus on proposing reliable character segmentation methods for
license plate recognition. Meanwhile, other studies concentrate on suggesting
license plate recognition methods without character segmentation, transforming
the problem into a sequence labeling task [25]. Leveraging the strengths of
improved YOLO models, license plate characters have been segmented and
recognized in [26]. The accuracy of character segmentation depends on the
segmentation performance and can be affected by external conditions like light
intensity, blurriness of the license plate, etc. These conditions can reduce
the accuracy of license plate recognition. Currently, most researchers apply
non-character segmentation methods. RPnet, proposed by Xu et al. [27], swiftly
and accurately predicts license plate bounding boxes, simultaneously
determining the corresponding license plate by extracting features of Regions
of Interest (ROIs) from different convolutional layers. This model surpasses
existing object detection and recognition models in both accuracy and speed,
achieving a 98.5% accuracy rate.

###  II-C Character Recognition methods

In order to recognize characters within images, many research groups [29] have
relied on image features for identification. The CRNN study [28] initially
combines CNN and RNN to extract sequential visual features from a specific
text image. These features are then fed directly into the CTC decoding
mechanism to predict the best character type for each time step. CTC [30] in
this context is a loss function used to train deep learning models. Most
methods recognize characters in a unidirectional manner. For example, Lee et
al. [32] encoded input text images horizontally into 2D sequential image
features, which are subsequently input into the corresponding regions with
semantic information assistance from the previous time step. To mitigate
mischaracterizations due to scene distortion and skewed distribution, Yang et
al. [31] introduced an improved module prior to character recognition. This
module employed a spatial transformation network with multiple control point
pairs. In our research framework, the CnOCR module is utilized, applying
unidirectional character recognition to accurately locate character features
and enhance the recognition performance of the model.

##  III PROPOSED METHOD

Figure 2: Overview of our proposed method.

Our proposed approach is depicted in Figure 2, comprising three main
components: (i) the YOLO model for license plate detection; (ii) the License
Plate Image Fusion algorithm for selecting the highest-quality license plate
image; and (iii) the OCR model for character feature extraction and license
plate recognition. The input consists of individual frames captured from
cameras, which are divided into different viewpoints (views) for each camera.
Each viewpoint becomes the input for a YOLO model. In this paper, we optimize
the YOLO license plate detection model based on experimental results from
various benchmark datasets. The quality of license plate images varies across
different views, including factors like angle, visibility, blurriness, and
distortion. Thus, we develop a License Plate Image Fusion algorithm to combine
similar license plate images into a single image with enhanced information.
Finally, the fused license plate image is passed through the OCR model for
license plate recognition.

###  III-A YOLO model

The YOLOv8 model is employed to detect license plates appearing within frames.
YOLOv8 is chosen due to its high accuracy in detection and fast processing
times, making it suitable for real-time applications. Moreover, the YOLOv8
model provides various versions with different sizes, allowing deployment in
diverse environments.

Our system processes high-resolution input images (3840 × 2160) decoded from
high-resolution videos. We collected images of various vehicle types and real
Vietnamese license plates to create a custom dataset. Subsequently, this
custom dataset was used to train the YOLOv8 model in order to construct two
custom detection pipelines. The detection model is capable of identifying
seven different object types (six vehicle types and different Vietnamese
license plates) within the input images. In this phase, vehicle types and
license plate occurrences are detected by the detection model. If a license
plate is detected, the license plate image is cropped and passed on to phase
2. In summary, the detection model is first called to identify vehicle types
and license plates. In cases where the input image contains a large number of
license plates, the iterative process of recognizing each license plate may
take longer compared to scenarios with only one license plate present in the
frame.

###  III-B Image Fusion algorithm

In evaluating object detection methods, the Intersection over Union (IoU)
metric [33] is commonly utilized. The IoU is a crucial measure for assessing
the accuracy of object detection results. The underlying principle of the IoU
is depicted in Figure 3. The IoU is calculated by dividing the area of
intersection between the predicted bounding box and the ground-truth bounding
box by the area of their union. This provides a quantitative measure of how
well the predicted bounding box aligns with the actual object location. To
enhance the IoU metric, the Generalized IoU (GIoU) was introduced to address
situations where the IoU loss becomes zero when the predicted bounding box and
the ground-truth bounding box do not directly overlap. Moreover, DIoU [33],
introduces the Euclidean distance between the center points of the predicted
and ground-truth bounding boxes based on the GIoU metric. This addition
further refines the evaluation by considering the spatial distance between the
bounding boxes, thereby aiding in speeding up the convergence of object
detection model training.

Figure 3: IoU score.

While the IoU metric has undergone multiple refinements across its various
iterations, it still may not be inherently suitable for the construction of
automated license plate recognition (LPR) models. This stems from the fact
that within LPR models, if a license plate is detected with an excess of
supplementary information (the detection area surpasses the actual license
plate area), subsequent license plate recognition models may encounter fewer
hindrances in accurately extracting characters from the detected license
plate. However, when a license plate is detected with information deficits
(the detection area is smaller than the genuine license plate area), this can
pose challenges for subsequent recognition modules, thereby resulting in
recognition inaccuracies. To expound upon this matter, we offer illustrative
examples as depicted in Figure 4. It is evident that the predicted bounding
box, as depicted in Figure 4, manifests a larger area than the actual license
plate region.

However, if the area of the predicted bounding box happens to be smaller than
the actual license plate area, it can result in certain characters not being
encompassed within the predicted region. Such information loss can lead to
errors in the final license plate recognition outcome. Consequently, models
based on loss functions utilizing the existing IoU metric are unable to
effectively address these issues, as they assign similar priorities to regions
with both information loss and surplus. A novel loss function based on the IoU
metric is imperative to achieve a more balanced treatment between information
loss and surplus, thereby providing a more effective means of handling these
challenges.

Figure 4: Some examples of license plate detection

After the IoU calculation, two regions are identified: (1) Non-overlapping
region; (2) Overlapping region. For the overlapping region, we employ an image
fusion method to generate an image with the best quality. Given the source
images denoted as $\mathbf{I_{1}}$ and $\mathbf{I_{2}}$, a DenseNet model is
trained to produce the fused image. The output of the feature extractor
comprises feature maps $\phi C_{1}(\mathbf{I_{1}}),...,\phi
C_{5}(\mathbf{I_{1}})$ and $\phi C_{1}(\mathbf{I_{2}}),...,\phi
C_{5}(\mathbf{I_{2}})$ where $C_{i}$ represents a specific layer within the
feature extractor and $\phi$ is the feature extractor. Subsequently, an
information measure is performed on these feature maps, resulting in two
measures denoted as $g\mathbf{I_{1}}$ and $g\mathbf{I_{2}}$. In the subsequent
processing, the degree of information preservation is denoted as $\omega_{1}$
and $\omega_{2}$. $\mathbf{I_{1}}$, $\mathbf{I_{2}}$, $\mathbf{I_{f}}$,
$\omega_{1}$, and $\omega_{2}$ are utilized in the loss function without
requiring ground truth labels. During the training phase, $\omega_{1}$ and
$\omega_{2}$ are computed to determine the loss function. Afterwards, a
DenseNet module is optimized to minimize the loss value. In the testing phase,
$\omega_{1}$ and $\omega_{2}$ need not be computed as the DenseNet model has
been optimized. Therefore, $\omega_{1}$ and $\omega_{2}$ are defined as:

$[\omega_{1},\omega_{1}]=softmax([\frac{g\mathbf{I_{1}}}{c},\frac{g\mathbf{I_{1}}}{c}])$
(1)

In this context, we employ the softmax function to map
$\frac{g\mathbf{I_{1}}}{c}$ and $\frac{g\mathbf{I_{2}}}{c}$ to real numbers
within the range of 0 to 1, ensuring that the sum of $\omega_{1}$ and
$\omega_{2}$ equals 1. Subsequently, $\omega_{1}$ and $\omega_{2}$ are
utilized in the loss function to control the degree of information
preservation of specific source images.

The loss function is primarily designed to preserve essential information and
to train a single model that can be applied to various tasks. It is defined as
follows:

$\mathcal{L}=\mathbb{E}(\omega_{1}\cdot
MSE_{\mathbf{I_{f}},\mathbf{I_{1}}}+\omega_{2}\cdot
MSE_{\mathbf{I_{f}},\mathbf{I_{2}}})$ (2)

This loss function is then utilized to train the feature aggregation model,
which combines features from multiple different frames into an optimized
feature representation for the license plate character recognition task in
images.

###  III-C OCR model

Figure 5 illustrates the chosen architecture of the CnOCR network for the
character recognition part. Initially, a convolutional layer with 40 kernels
of size 3 × 3 is applied to the input image, which is a matrix block, to
extract basic features. A subsequent pooling layer aims to reduce the
resolution by selecting the most prominent features within a 1 × 2 region. Two
additional convolutional sets (60 and 80 kernels) and max pooling layers are
added. However, the final pooling layer employs a filter size of 2 × 2.
Traditional architectures usually perform 2 × 2 pooling, halving both
dimensions. In contrast, we apply two pooling layers to halve only the height,
not the width. The reason is that the maximum number of predicted labels
corresponds to the size of the time axis of the last layer, which is the width
in our case. Due to the dense and overlapping nature of some license plates
characters, we incorporate only a 2 × 2 pooling layer. After the final pooling
layer, a fourth convolutional layer is added with an 80-sized kernel, followed
by a bidirectional LSTM layer with 100 hidden nodes.The LSTM layer is
instrumental in capturing contextual information and dependencies between
characters within the text while convolutional layers are used as feature
extractors to analyze the visual characteristics of characters and text
regions. Lastly, a dense layer with softmax activation transforms the 100
output nodes at each position into probabilities for the 53 (52 + blank)
target characters at each horizontal position for character recognition. The
key advantage of CnOCR lies in its fast prediction speed, achieving both
accuracy and prediction time of 0.03 seconds for a single license plate.

Figure 5: OCR model.

###  III-D The license plate dataset and configurations

The PTITPlates dataset consists of 500 license plate images labeled using the
LabelMe tool. We collected these images through cameras placed in various
industrial and road areas. The images capture different angles and have been
filtered to include only those with visible license plates, which aids in
training and testing the proposed model. The training parameters for our
proposed method are presented in Table I. The total trainable parameters of
the YOLOv8 model are around 11 million, while for the feature fusion and OCR
models, the parameter count is smaller. The optimization algorithm we employ
is Stochastic Gradient Descent, and the loss function used is cross-entropy.

TABLE I: Parameters of the proposed model: Parameters | Layers | Shapes
---|---|---
928 | Conv | [3, 32, 3, 2]
18560 | Conv | [32, 64, 3, 2]
29056 | C2f | [64, 64, 1, True]
73984 | Conv | [64, 128, 3, 2]
197632 | C2f | [128, 128, 2, True]
295424 | Conv | [128, 256, 3, 2]
788480 | C2f | [256, 256, 2, True]
1180672 | Conv | [256, 512, 3, 2]
1838080 | C2f | [512, 512, 1, True]
656896 | SPPF | [512, 512, 5]
0 | Upsample | [None, 2, ’nearest’]
0 | Concat | [1]
591360 | C2f | [768, 256, 1]
0 | Upsample | [None, 2, ’nearest’]
0 | Concat | [1]
148224 | C2f | [384, 128, 1]
147712 | Conv | [128, 128, 3, 2]
0 | Concat | [1]
493056 | C2f | [384, 256, 1]
590336 | Conv | [256, 256, 3, 2]
0 | Concat | [1]
1969152 | C2f | [768, 512, 1]
2116435 | Detect | [1, [128, 256, 512]]
Model summary: 225 layers, 11135987 parameters

We utilize loss value transformation graphs over time to evaluate and compare
the performance of different loss functions. These graphs provide a visual
representation that allows us to observe and analyze the variation of the loss
function during training. We present three graph images in Figure 6,
corresponding to three different loss functions: (i) Localization Loss,
depicting the training process of license plate detection; (ii) Classification
Loss, illustrating the training of license plate classification and image
fusion model; and (iii) Connectionist Temporal Classification Loss, indicating
the character recognition process within the license plate.

Specifically, for the YOLOv8 loss function, the Variational Focal Loss (VFL)
is employed as the classification loss, while the combination of Distribution
Focal Loss (DFL) and Complete Intersection over Union Loss (CIOU) serves as
the segmentation loss. Similarly to conventional classification tasks, in the
license plate classification step, we employ the Categorical Cross-Entropy
loss with the following formula (equation 3):

$\mathcal{L}_{CE}=-\sum\frac{1}{N}(y_{true}*log(y_{pred}))$ (3)

In which $y_{true}$ represents the ground truth vector (one-hot encoding) of
the sample with a size of $C$, which is the number of different classes,
$y_{pred}$ is the predicted vector of the sample and $N$ is the number of data
samples.

Finally, in the recognition model, we utilize the loss function with the
general formula of CTC as follows:

$\mathcal{L}_{CTC}=-log(P(Y|X))$ (4)

Where $P(Y|X)$ represents the probability of the actual output sequence $Y$
given the input sequence $X$.

The results of the training process are depicted in Figure 6. The loss values
and accuracy during the training process exhibit a pronounced fluctuation in
the initial epochs, gradually stabilizing in the subsequent epochs, indicates
that the model is learning and improving over time. If the loss values and
accuracy exhibit instability during training then proposed model may not be
suitable for the dataset. Our proposed model tends to converge to an optimal
value after around 20 epochs. The training process was concluded after 25
epochs. The validation loss curves are generally close to the training loss
curves,implying that the model is not overfitting to the training data.
Additionally, the gradual, sustained observed in the validation loss curves
signifies a continuous enhancement in the model’s capability to generalize and
excel when applied to unobserved data.

Figure 6: Transformation of the loss function during the training process
Figure 7: Confusion matrix of the proposed method.

##  IV EXPERIMENTAL RESULTS

###  IV-A Accuracy Evaluation

We evaluated the models using the F1 score, considering equal importance in
the accurate classification of each class. From the results presented in Table
II, it can be observed that our proposed method achieves higher F1 scores
compared to the baseline methods, such as the YOLOv5 network which is
currently considered one of the best methods, as well as other methods like
YOLOv8 with Tesseract and YOLOv8 with CnOCR. This performance difference can
be attributed to the variations in the angles of license plates and the impact
of weather conditions. Our model effectively identifies the partitioning
mechanism using blocks and image fusion, resulting in improved performance.
The F1 score for the YOLOv5 network is only 75.2% with the PTITPlates dataset
and 78.6% with the Stanford Cars dataset. In contrast, our proposed method
achieves F1 scores of 91.3% with the PTITPlates dataset and 90.8% with the
Stanford Cars dataset. This phenomenon arises due to the presence of
substantial noise within the Stanford Cars dataset and its limited variety of
viewing angles, as compared to the PTITPlates dataset. This observation
underscores a vulnerability commonly associated with datasets that lack
diversity in viewing angles. The detailed confusion matrix of the proposed
license plate detection model is provided in Figure 7. As displayed in the
confusion matrix, the proposed method accurately categorized the majority of
specific license plates, as evidenced by elevated counts of True Positives
(TP). However, it encounters challenges in certain instances, particularly
when confronted with blurred plates, leading to the presence of False
Positives (FP) and False Negatives (FN).

TABLE II: comparision results Dataset | Method | F1-Score
---|---|---
Stanford Cars | YOLOv5 + Base OCR | 78.6
| YOLOv8 + Tesseract OCR | 80.3
| YOLOv8 + CnOCR | 86.3
| Our proposed method | 90.8
PTITPlates | YOLOv5 + Base OCR | 75.2
| YOLOv8 + Tesseract OCR | 82.9
| YOLOv8 + CnOCR | 85.2
| Our proposed method | 91.3

###  IV-B Real-world testing

In this experiment, we also integrated the model into a practical application.
Some images of our model’s predictions are shown in Figure 8. We deployed the
system to handle 30 cameras, grouped into 10 columns, to monitor vehicle
entries and exits in an industrial zone. Due to the substantial volume of
incoming images, we designed a distributed system with 10 integrated API
endpoints, each utilizing our model. On average, each model processed data
from 3 cameras, achieving a license plate detection latency of 0.1 seconds.
This result is quite impressive for the deployment of a license plate
detection and recognition system.

Figure 8: Some examples of predictions made by our model

##  V CONCLUSION

In this paper, we proposed a feature fusion model from multiple perspectives
based on the YOLOv8 and CnOCR architectures for license plate recognition.
Through evaluations on various datasets, experimental results demonstrated
that the proposed model achieved impressive results, with an F1 score of 91.3%
on the self-collected PTITPlates dataset. Notably, this dataset is noisy and
affected by weather conditions, resulting in poor image quality of license
plates. The proposed model showcased robust performance across diverse
contexts with high accuracy. In the future, Generative Adversarial Networks
(GANs) could be employed for data augmentation, simulating license plates from
different regions to address class imbalance issues among Vietnamese character
groups within the dataset. This could potentially enhance accuracy.
Additionally, self-supervised learning models, such as zero-shot learning,
could be utilized to fine-tune the network based on the localization accuracy
of specific character features within license plates, potentially leading to
even better results.

## References

  * [1] I. O. Olayode, L. K. Tartibu, M. O. Okwu, and U. F. Uchechi, “Intelligent transportation systems, un-signalized road intersections and traffic congestion in Johannesburg: A systematic review,” Procedia CIRP, vol. 91, no. March, pp. 844–850, 2020, doi: 10.1016/j.procir.2020.04.137.
  * [2] J. Shashirangana, H. Padmasiri, D. Meedeniya and C. Perera, ”Automated License Plate Recognition: A Survey on Methods and Techniques,” in IEEE Access, vol. 9, pp. 11203-11225, 2021, doi: 10.1109/ACCESS.2020.3047929.
  * [3] A. Vaishnav and M. Mandot, Template Matching for Automatic Number Plate Recognition System with Optical Character Recognition, vol. 933. Springer Singapore, 2020. doi: 10.1007/978-981-13-7166-0_69.
  * [4] Q. Lu, Y. Liu, J. Huang, X. Yuan, and Q. Hu, “License plate detection and recognition using hierarchical feature layers from CNN,” Multimed. Tools Appl., vol. 78, no. 11, pp. 15665–15680, 2019, doi: 10.1007/s11042-018-6889-1.
  * [5] I. Slimani, A. Zaarane, W. Al Okaishi, I. Atouf, and A. Hamdoun, “An automated license plate detection and recognition system based on wavelet decomposition and CNN,” Array, vol. 8, no. August, p. 100040, 2020, doi: 10.1016/j.array.2020.100040.
  * [6] Y. Kessentini, M. D. Besbes, S. Ammar, and A. Chabbouh, “A two-stage deep neural network for multi-norm license plate detection and recognition,” Expert Syst. Appl., vol. 136, pp. 159–170, 2019, doi: 10.1016/j.eswa.2019.06.036.
  * [7] L. Ma and Y. Zhang, “Research on vehicle license plate recognition technology based on deep convolutional neural networks,” Microprocess. Microsyst., vol. 82, no. January, p. 103932, 2021, doi: 10.1016/j.micpro.2021.103932.
  * [8] V. Burkpalli, A. Joshi, A. B. Warad, and A. Patil, “Automatic Number Plate Recognition Using Tensorflow and Easyocr,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 04, no. 09, pp. 493–501, 2022, [Online]. Available: www.irjmets.com
  * [9] O. I. Abiodun et al., “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access, vol. 7, pp. 158820–158846, 2019, doi: 10.1109/ACCESS.2019.2945545.
  * [10] X. Y. Zhang, C. L. Liu, and C. Y. Suen, “Towards Robust Pattern Recognition: A Review,” Proc. IEEE, vol. 108, no. 6, pp. 894–922, 2020, doi: 10.1109/JPROC.2020.2989782.
  * [11] L. S. Bernardo et al., “Handwritten pattern recognition for early Parkinson’s disease diagnosis,” Pattern Recognit. Lett., vol. 125, pp. 78–84, 2019, doi: 10.1016/j.patrec.2019.04.003.
  * [12] A. Caggiano, J. Zhang, V. Alfieri, F. Caiazzo, R. Gao, and R. Teti, “Machine learning-based image processing for on-line defect recognition in additive manufacturing,” CIRP Ann., vol. 68, no. 1, pp. 451–454, 2019, doi: 10.1016/j.cirp.2019.03.021.
  * [13] T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS J. Photogramm. Remote Sens., vol. 173, no. July 2020, pp. 24–49, 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
  * [14] W. Lu, J. Li, J. Wang, and L. Qin, “A CNN-BiLSTM-AM method for stock price prediction,” Neural Comput. Appl., vol. 33, no. 10, pp. 4741–4753, 2021, doi: 10.1007/s00521-020-05532-z.
  * [15] A. Tourani, A. Shahbahrami, S. Soroori, S. Khazaee, and C. Y. Suen, “A robust deep learning approach for automatic Iranian vehicle license plate detection and recognition for surveillance systems,” IEEE Access, vol. 8, pp. 201317–201330, 2020, doi: 10.1109/ACCESS.2020.3035992.
  * [16] D. M. F. Izidio, A. P. A. Ferreira, H. R. Medeiros, and E. N. d. S. Barros, “An embedded automatic license plate recognition system using deep learning,” Des. Autom. Embed. Syst., vol. 24, no. 1, pp. 23–43, 2020, doi: 10.1007/s10617-019-09230-5.
  * [17] P. Mukhija, “Wavelet Transform and Morphological Processing Based License Plate Localization,” J. Algebr. Stat., vol. 13, no. 3, pp. 2732–2738, 2022.
  * [18] A. O. Vuola, S. U. Akram, and J. Kannala, “Mask-RCNN and u-net ensembled for nuclei segmentation,” Proc. - Int. Symp. Biomed. Imaging, vol. 2019–April, no. Isbi, pp. 208–212, 2019, doi: 10.1109/ISBI.2019.8759574.
  * [19] I. S. Isa, M. S. A. Rosli, U. K. Yusof, M. I. F. Maruzuki, and S. N. Sulaiman, “Optimizing the Hyperparameter Tuning of YOLOv5 for Underwater Detection,” IEEE Access, vol. 10, pp. 52818–52831, 2022, doi: 10.1109/ACCESS.2022.3174583.
  * [20] L. Qiao, Y. Zhu, and H. Zhou, “Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms,” IEEE Access, vol. 8, pp. 104292–104302, 2020, doi: 10.1109/ACCESS.2020.2993937.
  * [21] A. Imran, J. Li, Y. Pei, J. J. Yang, and Q. Wang, “Comparative Analysis of Vessel Segmentation Techniques in Retinal Images,” IEEE Access, vol. 7, pp. 114862–114887, 2019, doi: 10.1109/ACCESS.2019.2935912.
  * [22] Y. Zhou, S. Bai, C. Wang, X. Chen, E. Fishman, and A. L. Yuille, “Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation,” pp. 10672–10681.
  * [23] P. M. M. Pereira et al., “Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem,” Biomed. Signal Process. Control, vol. 57, p. 101765, 2020, doi: 10.1016/j.bspc.2019.101765.
  * [24] K. Gao et al., “Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images,” Med. Image Anal., vol. 67, p. 101836, 2021, doi: 10.1016/j.media.2020.101836.
  * [25] J. C. W. Lin, Y. Shao, Y. Djenouri, and U. Yun, “ASRNN: A recurrent neural network with an attention model for sequence labeling,” Knowledge-Based Syst., vol. 212, no. xxxx, p. 106548, 2021, doi: 10.1016/j.knosys.2020.106548.
  * [26] U. Upadhyay, F. Mehfuz, A. Mediratta, and A. Aijaz, “Analysis and Architecture for the deployment of Dynamic License Plate Recognition Using YOLO Darknet,” 2019 Int. Conf. Power Electron. Control Autom. ICPECA 2019 - Proc., vol. 2019–November, pp. 1–6, 2019, doi: 10.1109/ICPECA47973.2019.8975456.
  * [27] L. Xu, J. Jiang, and L. Liu, “RPNet: A Representation Learning-Based Star Identification Algorithm,” IEEE Access, vol. 7, pp. 92193–92202, 2019, doi: 10.1109/ACCESS.2019.2927684.
  * [28] S. Jiao, “A CRNN-GRU BASED REINFORCEMENT LEARNING APPROACH TO AUDIO CAPTIONING MoE Key Lab of Artificial Intelligence SpeechLab , Department of Computer Science and Engineering Decoder - 1 La y er 512 Unit GRU,” no. November, pp. 225–229, 2020.
  * [29] T. T. H. Nguyen, A. Jatowt, M. Coustaty, and A. Doucet, “Survey of Post-OCR Processing Approaches,” ACM Comput. Surv., vol. 54, no. 6, 2021, doi: 10.1145/3453476.
  * [30] M. Rais-Zadeh et al., “Gallium nitride as an electromechanical material,” J. Microelectromechanical Syst., vol. 23, no. 6, pp. 1252–1271, 2014, doi: 10.1109/JMEMS.2014.2352617.
  * [31] M. Yang et al., “Symmetry-constrained rectification network for scene text recognition,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2019–October, pp. 9146–9155, 2019, doi: 10.1109/ICCV.2019.00924.
  * [32] P. Lyu, Z. Yang, X. Leng, X. Wu, R. Li, and X. Shen, “2D Attentional Irregular Scene Text Recognizer,” 2019, [Online]. Available: http://arxiv.org/abs/1906.05708
  * [33] Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-IoU loss: Faster and better learning for bounding box regression,” AAAI 2020 - 34th AAAI Conf. Artif. Intell., no. 2, pp. 12993–13000, 2020, doi: 10.1609/aaai.v34i07.6999.
  * [34] J. M. Jayoma, E. S. Moyon, and E. M. O. Morales, “OCR Based Document Archiving and Indexing Using PyTesseract: A Record Management System for DSWD Caraga, Philippines,” 2020 IEEE 12th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. HNICEM 2020, 2020, doi: 10.1109/HNICEM51456.2020.9400000.

|  Dat Tran-Anh received the B.E. degree in security information and the M.S.
degree in computer science from the Posts and Telecommunications Institute of
Technology, Hanoi, in 2020 and 2022, respectively. He is currently pursuing
the Ph.D. degree in Computer Science at Vietnam Academy of Science and
Technology. Since 2023, he has been a lecturer in the Artificial Intelligence
department, Thuyloi University. His research interests include image
processing and deep learning.
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|  Khanh Linh Tran is a third year student at Posts and Telecommunications
Institute of Technology majoring in Information Technology. Her research
interests are image processing and applications of machine learning/deep
learning.
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|  Hoai-Nam Vu received the B.E. degrees in Electronic and Telecommunication
engineering from the Hanoi University of Science and Technology, Hanoi,
Vietnam in 2013 and the M.S. degree in Electronic and Computer engineering
from Chonnam National University, Gwangju, South Korea, in 2015. He is
currently pursuing the Ph.D. degree in Computer Science at Posts and
Telecommunications Institute of Technology, Hanoi. Since 2016, he has been a
lecturer with Computer Science Department, Posts and Telecommunications
Institute of Technology, Vietnam. His research interests include UAV image
processing, machine learning, and deep learning.
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