Automatic Number Plate Recognition (ANPR) systems have become an increasingly crucial technology for law enforcement, traffic management, and security purposes. These systems, based on computer vision and machine learning techniques, are capable of automatically extracting and recognizing license plate information from images or video footage.

1. Automatic Number Plate Recognition System in Bangla using Deep
Learning model.
Most.Jannat-Ul-Ferdoush(200101068),Mst.Habiba Hena Sumi (200101070),
Md. Talath Un Nabi (200101076)
Course Code: CSE 4132Course Title: Artificial Neural Networks and Fuzzy Systems Sessional
Semester: Winter 2023
*Department of Computer Science and Engineering, Bangladesh Army University of Science
and Technology (BAUST)
Abstract
Traffic control and vehicle owner identification become major problems in Bangladesh.
Most of the time it is difficult to identify the driver or the owner of the vehicles who violate
the traffic rules or do any accidental work on the road. So, this work for Bangla number
plate detection. We use 3 different model for number plate detection and easy OCR for
Bangla character recognition. The trained model are i.YOLOv5(You Only Look
Once),ii.VGG16 and iii.Inception_ResNet-V2 .1st
model for automatic number plate
recognition with (ANPR) YOLOv5 system, and text detect with OCR , here detection
confidence rate is 89% . 2nd
paper model for number plate recognition with (ANPR)
VGG16 model system, and text detect with OCR , here detection confidence rate is 79.5%.
3rd
model for automatic number plate recognition with (ANPR) ANN using Inceptiop-
resnetV2 model ,and text detect with OCR , here detection confidence rate is 64.66%.Better
model is YOLOv5 for this dataset. For speed, we tested our model on Google Colaboratory’s
free GPU and attained a speed of 7 frames per second while detecting and recognizing the
license plate numbers.
Keywords—Automatic Number Plate Recognition (ANPR), Optical Character Recognition
(OCR), License Plate (LP), YOlOv5, Inception-ResNetv2, VGG16, deep neural
network(DNN), Convolutional Neural Network(CNN).
1. Introduction
Automatic Number Plate Recognition is a vital part of controlling the traffic system
intelligently and efficiently. The use of the automated parking management system is
increasingly becoming popular in Bangladesh. Use the detect number for toll collection,
parking lot management, enterprise entrance management, border surveillance, effective
traffic control and security applications such as access control to restricted areas and
tracking of wanted vehicles digitally.[1] ANPR systems contain three core steps: number
plate area detection, breakdown of characters, and Optical Character Recognition
(OCR).[2] Different methodologies have been used for ANPR systems, including
Artificial Neural Network, Probabilistic neural network, Optical Character Recognition,
MATLAB, Configurable method, Sliding Concentrating window, Back-Propagation
Neural Network, and Support Vector Machine. ANPR are born on joint methodologies
such as Artificial Neural Network, Probabilistic neural network, Optical Character

2. Recognition, MATLAB, Configurable method, Sliding Concentrating window, Back-
Propagation Neural Network, Support Vector Machine, Inductive Learning.[2] Optical
Character Recognition method, which is a widely used tool for mechanical or electronic
conversion of images of typed, handwritten or printed text into machine-encoded text,
whether from a scanned document, a photo of a document, a scene-photo or from subtitle
text superimposed on an image. OCR software pre-processes the images to enhance the
chances of successful recognition. The two non-intersecting images data sets were used
to copy the actual-world cases where the neural network will be subjected to.[2] Here
using 3 model for same dataset. One is YOLO version 5. YOLOv5 is one of the ‘state of
the art’ algorithms for real time object detection and classification.[3] Using YOLOv5 as
our CNN model, we achieved confidence up to 89% in our dataset which is best from
other 2 model confidence. Here we use 3 different type model for different dataset and
get detect number plate.YOLOv5 is a real-time object detection model that uses a single
convolutional neural network to predict the class and bounding box coordinates of
multiple objects in an image. It is a smaller and faster version of previous YOLO models
while maintaining high confidence. 2nd
model is Inception_ResNetv2.Inception model is
a deep neural network architecture used for image and video classification tasks. It
utilizes a combination of convolutional layers with various filter sizes to extract features
from images at different scales. ResNetv2 is a deep neural network architecture that
includes skip connections, enabling training of very deep neural networks up to hundreds
of layers. It uses residual learning to address the vanishing gradient problem and improve
model performance. We combine this 2 model for object detection. Another model is
VGG16. VGG16 is a deep convolutional neural network architecture used for image
classification tasks. It consists of 16 convolutional layers with small filter sizes and max
pooling layers, followed by fully connected layers for classification. We use OCR for
Character recognition in all model. We compare our three model for get character from
bangla number plate perfectly and perform work. The most popular style of one line
license plate format throughout the world, Bangla license plate is composed of two
License (BRTA) standard number plates).[3] The vehicle category is indicated by the
vehicle class letter (খ,গ,ঘ,এ,) .This kind of digital plate contains two rows. At lower
row, there are numbers and at the upper row, there are alphabets. In the lower row, there
remain two separate parts containing six digits.[8]
2. Literature Review
Automatic License Plate Recognition (ALPR) is a type of technology that enables
computer systems to study automatically the registration number (license number) of
vehicles from digital pictures. So many works doing in this side. CNN, ANN, Deep
learning models can detect easily the number plate. Although here some limitation has
present. In [1], authors proposed Bangla automatic number plate recognition system
using artificial neural network to detection for various climate condition and accuracy
rate is 95% with an average processing time of 0.75 seconds. A robust feature extraction
technique is applied to extract the feature from each characters which is invariant to the
rotation and scaling. In [2], authors proposed English automatic number plate recognition
system using Back-Propagation Neural Network, Support Vector Machine accuracy rate

3. is 82.5% .OCR use for character recognition . In [3], authors proposed bangle automatic
number plate recognition system using) convolutional neural network (CNN) based
model YOLO system, and text detect with OCR , here detection accuracy rate is 99.5%.
In [4], authors proposed bangle automatic number plate recognition system using) ANN
using feature extraction model system, and text detect with OCR, here detection accuracy
rate is 94.45%. In [4], authors proposed bangle automatic number plate recognition
system using) ANN using feature extraction model system, and text detect with OCR,
here detection accuracy rate is 94.45%. In [5], authors proposed bangle automatic
number plate recognition system model YOLOv3 system, and text detect with CNN
model, here detection accuracy rate is 97.5%. In [6], authors proposed bangle automatic
number plate recognition system using Support Vector Machine has been used for
classification, and text detect with OCR, here detection accuracy rate is 92.5%. In [7],
authors proposed bangle automatic number plate recognition system using multilayer
feed-forward network, and MLP network to recognize each characters and words to
identify the number plate., here detection accuracy rate is 75.51%. In [8], authors
proposed bangle automatic number plate recognition system using YOLO model system,
and text detect with CNN, here detection accuracy rate is 81%. In [9], authors proposed
bangle automatic number plate recognition system using YOOv3, model system, and text
detect with OCR, here detection accuracy rate is 88.89%. In [10], authors proposed
bangle automatic number plate recognition system using SSD model system, and text
detect with CNN, here detection accuracy rate is 97.5%. In [11], authors proposed bangle
automatic number plate recognition system using ANN Deep Convolutional Neural
Network (DCNN) model which is a single short detection, here detection accuracy rate is
99%. In [12], authors proposed bangle automatic number plate recognition system using
CNN using feature extraction model system, here detection accuracy rate is 89%.
3. Why we choose these models
YOLOv5 VGG16
Inception-ResNet
v2
Architecture Object Detection
Convolutional
Neural Network
Convolutional
Neural Network
Single Shot Detector Yes No No
Number of
Parameters
Varies based on the
model size
138 million
parameters
55.8 million
parameters
Object Detection
Average
Precision(AP)
High Moderate High
Performance
State-of-the-art in
object detection
Well-established in
image classification
High performance in
various tasks
Table1:model details

4. 4. Device we used
5. Dataset Description
Total Images: 20,000
Annotations:20,000
Unique Images: 4,000
Augmentations: random_brightness, horizontal_flip, vertical_flip, rotation, grayscale
A license plate same dataset used for train our models. We use 20,000 data and use 5 type
augmentations, which are randomly brightness, grayscale, horizontal flip, vertical flip, rotation.
We separate 80% data for training (for two models, another model use 90%) and 20% for
validation and 5 selected data for testing from overall data. Every license plate has two line in
the first line and 6 numbers in the second line, resulting in 9450 words and characters
(alphanumeric symbols) that have been annotated with bounding boxes. Our dataset has also
been manually augmented to avoid over fitting. For that, we randomly translated and scaled up
to 20% of the captured image sizes.

5. Fig.1.our dataset
Fig.2.annotation code for bounding box

6. 6. Data Augmentation
Fig.3. after perform data augmentation
7. Methodology
For the complete detection process of characters from the license plate, three stages were split.
Initially, Load image. Then crop image to find bounding box for predicted coordination, then the
image was augmentation to get perfect predicted value, Process image as our model parameter
and normalize it. Separate 80% data for train and rest 20% for validation. Then apply our model
for train data and validation data. Then text data and predict output. Then create pipeline or
threshold and apply easy OCR for character reorganization. Flow diagram of the proposed
system of license plate recognition is shown in Fig 3.
Data Preprocessing: Load data deletion. Then annotation was completed for the number plate
of all vehicles in the first dataset. To annotate the dataset, we utilize labeling. After annotation,
we contrast Normalization for license plate recognition.
Fig.1.Normalize image
Augmentation: convert real image into grayscale, Horizontal flip, vertical flip, rotation, random
brightness. Show in fig.4

7. Apply Models: YOLOv5, Inception-resNetv2,VGG16 and Train Dataset abjectness’ was used
by model for bounding box prediction and cost function measurement. For each bounding box
using logistic regression, models predict an object score. The cost function is calculated
differently in models. And those models was used for detecting the number plates which were
trained on the first dataset after the annotation.
License Plate Detection and Localization:Following that, after YOLOv5(10),Inception-
resNetv2(180),VGG16(180) training, the best-predicted model was used. The experiment could
save the best epoch of training. After epochs, the targeted detection model was found while
training the data.
Fig. 4. Flow diagram of our experiment
Crop License Plate using Predicted Bounding Box :The saved model was used to detect the
license plate. And then, predicted bounding box coordinate was used to crop license plates from
images.
Apply threshold for Character Segmentation: The next step was to complete the
segmentation. For segmentation, threshold algorithm was used. Threshold is an old but effective
one for the segmentation process.
Apply EasyOCR model for Character Recognition: EasyOCR supports Bangla language for
optical character recognition (OCR) and can recognize printed and handwritten Bangla text from
images or videos. It uses a deep learning model trained on Bangla characters for high confidence.

8. 8. Models
Hyperparameters:
YOLOv5 VGG16
Inception-Inception
ResNet v2
Learning Rate 0.01 0.001 1e-4
Optimizer
SGD (Stochastic
Gradient Descent)
Adam Adam
Number of
Parameters
7022326 17,099,140 73,663,490
metrics
mAP50(Mean Average
Precision)
accuracy accuracy
Table.2. Hyperparameters
Train/Test Split
YOLOv5 VGG16 Inception-ResNet v2
Training 80% 90% 80%
Testing 20% 10% 20%
Random State 0 1 0
Table.3. Train Test Data Split
Object detect using YOLOv5
Our model has use total 214 layer where 127is convolution layers. For detecting the license
plate, we trained our model for 10 epochs. For segmenting and recognizing the license plate, we
trained our model for 10 epochs. In the time of training, the hyper parameters for our model,
which are given below-
I. Batch size = 20
II. Epoch=50
III. Momentum = 0.937
IV. Weight decay=0.00046875=0.0005
V. Learning rate = 0.01
VI. Optimizer =SGD
VII. Loss= mse (mean square error )
Model details

9. The architecture of YOLOv5 is composed of several convolutional layers with various filter
sizes, strides, and activation functions. The model is based on a modified version of the Efficient
Net backbone architecture, which consists of a series of convolutional blocks with varying
depths and widths. Here is an overview of the key layers and activation functions used in
YOLOv5:
Fig.5.Model summary for YOLOv5
1. Convolutional Layers: The YOLOv5 architecture includes many convolutional
layers, including 1x1, 3x3, and 5x5 filters, as well as dilated and transposed
convolutions. These layers extract features from the input image at different
spatial scales.
2. Activation Functions: YOLOv5 uses the Mish activation function, which is a
smooth and non-monotonic function that has been shown to improve performance
compared to traditional activation functions like ReLU. The Mish function is
defined as
f(x) = x * tanh(softplus(x)).
3. Spatial Pyramid Pooling: YOLOv5 incorporates a Spatial Pyramid Pooling (SPP)
layer, which allows the network to capture features at multiple scales. The SPP
layer pools features from different regions of the feature map at different scales
and concatenates them into a single vector.
4. Backbone Architecture: YOLOv5 uses a modified version of the Efficient Net
backbone architecture, which includes a series of convolutional blocks with
varying depths and widths. This allows the model to efficiently learn features at
different scales while minimizing the number of parameters.
5. Object Detection Head: The final layers of YOLOv5 include a set of
convolutional layers that predict the bounding boxes and class probabilities for
each object in the input image. These layers use anchor boxes to predict the
location and size of each object and are trained using a combination of
classification and regression loss functions.

10. YOLOv5 is a powerful and efficient object detection model that combines state-
of-the-art convolutional neural network architectures with advanced features like
Spatial Pyramid Pooling and the Mish activation function.
Testing code:
It looks like the code you provided is attempting to perform object detection on an image using
the YOLO algorithm and then display the results using Plot. However, the YOLO predictions
function that is being called is not defined in the code snippet you provided, Assuming that the
YOLO predictions function is defined elsewhere and correctly implemented, this code should
display the original image with the detected objects overlaid on it, and print out the text that was
detected in any license plates that were identified. Without additional context or information
about the YOLO predictions function, it is difficult to provide more specific feedback or advice.
Output:

11. Fig.6.detect bounding box with confidence with 89%
Character reorganization code:
This code defines a function called 'extract text' that extracts text from an image region specified
by a bounding box. The code uses the EasyOCR library to perform OCR on the specified region.
Here's how the code works:
1. The 'easyocr' library is imported.
2. The 'extract text' function is defined with two parameters: 'image' and
'bounding box'. 'image' is the input image from which the text needs to be

12. extracted. 'bounding box' is a tuple that specifies the bounding box
coordinates of the region from which the text needs to be extracted.
3. The bounding box coordinates are used to extract the region of interest
(ROI) from the input image.
4. If the ROI has a shape of (0,0), meaning it is empty, the function returns
the string 'no number'.
5. If the ROI is not empty, the EasyOCR 'read text' function is used to extract
the text from the ROI.
6. The resulting text is concatenated into a single string and stripped of any
leading/trailing white space.
The function returns the extracted text.
Fig.7.recognized bangle character
Object detect using Inception-Resnetv2
Our model has 743 convolutional layers. For detecting the license plate, we trained our model for
180 epochs. For segmenting and recognizing the license plate, we trained our model for 180
epochs. In the time of training, the hyper parameters is given below:
I. Batch size = 10
II. Epoch=180
III. Momentum = 0.7
IV. Weight decay=
V. Learning rate = 1e-4
VI. Optimizer=ADAM
VII. Loss function=mse(mean square error)
Model details
This model for image classification using transfer learning with the InceptionResNetV2 pre-
trained model. Here is a breakdown of the key components of the code:

13. Fig.8.model for Inception-ResNetv2
1. InceptionResNetV2 model: This is a pre-trained image classification model included in
the Keras library. The InceptionResNetV2 function is used to load the pre-trained
weights, and the include top=False argument is used to exclude the final fully connected
layer of the model.
2. Head model: The head model variable defines the fully connected layers that will be
added to the pre-trained model. The output from the InceptionResNetV2 model is passed
as input to the head model.
3. Flatten layer: The Flatten layer is used to flatten the output from the InceptionResNetV2
model into a 1-dimensional vector.
4. Dense layers: The Dense layers define the fully connected layers in the head model. The
first Dense layer has 500 units with ReLU activation, the second has 250 units with
ReLU activation, and the final layer has 4 units with sigmoid activation.
5. Model: The Model function is used to define the final model architecture, which includes
the InceptionResNetV2 model as the base and the head model on top of it. The inputs
argument specifies the input tensor shape, and the outputs argument specifies the output
tensor shape.
6. loss function and optimizer to be used during training. Specifically, it uses mean squared
error (mse) as the loss function and the Adam optimizer with a learning rate of 1e-4.
This code creates a transfer learning model that uses the pre-trained InceptionResNetV2 model
to extract features from images and then applies a set of fully connected layers to make
predictions about the input image. The model is trained using a binary cross-entropy loss
function and the Adam optimizer.

14. Testing code:
The code defines a function called object detection that takes an input image, performs object
detection using a pre-trained model, draws a bounding box around the detected object, and
returns the modified image and the coordinates of the bounding box. The code then displays the
modified image and a cropped image of the detected object. This code can be useful for
performing object detection and extracting detected objects from images.
Fig.9.crop bounding box

15. Output:
Fig.10.detect bounding box with confidence with 61.95%
Character reorganization code
This code uses the EasyOCR library to perform optical character recognition (OCR) on an
image. Specifically, it uses the 'bn' (Bangla) language model to read text from the image.
Here's a breakdown of the code:

16. 1. The 'easyocr' library is imported.
2. A reader object is created with the 'bn' language model.
3. The 'read text' function is used to read text from the image.
4. The resulting text is stored in the 'text' variable by looping through the text detection
results and concatenating the detected text.
Finally, the detected text is printed to the console.
Fig.11.recognize bangle character
Object detect using VGG16
Our model has 13 convolutional layers and 3 fully connected layers. For detecting the license
plate, we trained our model for 180 epochs. For segmenting and recognizing the license plate, we
trained our model for 180 epochs. In the time of training, the hyper parameters are given below:
I. Batch size = 32
II. Epoch=180
III. Momentum = 0.7
IV. Weight decay=
V. Learning rate = 0.001
VI. Optimizer=ADM
VII. Loss function= mse(mean square error)
Model details
The model is a Sequential model with the VGG16 architecture as the base.

17. Fig.12.Model summary for VGG16
The model is a Sequential model with the VGG16 architecture as the base. The VGG16 layers
are pre-trained on the ImageNet dataset and only the fully connected layers are added on top of
it. The fully connected layers have 128, 128, and 64 units respectively with ReLU activation
function, and the output layer has 4 units with sigmoid activation function. The second last layer
of the VGG16 model is frozen and its weights are not updated during the training. The model is
compiled with the mean squared error (MSE) loss function and the Adam optimizer with a
learning rate of 0.001. The IMAGE_SIZE variable is not defined in the code snippet, so it is
unclear what the input image size is. This is the pre-trained VGG16 model from the ImageNet
dataset, which consists of 13 convolutional layers followed by 3 fully connected layers.
1. Flatten: This layer flattens the output of the previous layer into a 1D
tensor, which can be fed into the fully connected layers.
2. Dense: This fully connected layer has 128 units and uses the ReLU
activation function.
3. Dense: Another fully connected layer with 128 units and ReLU activation
function.
4. Dense: Yet another fully connected layer with 64 units and ReLU
activation function.
5. Dense (output layer): This is the final fully connected layer with 4 units
and sigmoid activation function, which is used for binary classification.
6. The second last layer of the VGG16 base is frozen and its weights are not
updated during the training. The model is compiled with the mean squared

18. error (MSE) loss function and the Adam optimizer with a learning rate of
0.001.
Testing code:
The code defines a function called object detection that takes an input image, performs object
detection using a pre-trained model, draws a bounding box around the detected object, and
returns the modified image and the coordinates of the bounding box. The code then displays the
modified image and a cropped image of the detected object. This code can be useful for
performing object detection and extracting detected objects from images.
Fig.13.crop bounding box

19. Fig.14.detect bounding box with confidence with 77.27%
This code uses the EasyOCR library to perform optical character recognition (OCR) on an
image. Specifically, it uses the 'bn' (Bangla) language model to read text from the image.
Here's a breakdown of the code:
1. The 'easyocr' library is imported.
2. A reader object is created with the 'bn' language model.
3. The 'read text' function is used to read text from the image.
4. The resulting text is stored in the 'text' variable by looping through the text detection
results and concatenating the detected text.
Finally, the detected text is printed to the console.
Fig.12. recognized bangle character

20. 9. Result and Analysis
YOLOv5 VGG16
Inception-ResNet
v2
Batch Size 20 32 10
Epoch 50 180 180
Confidence(%) 89 77.27 61.95
Table.4. Summary of the confidence for minimum epoch size
As we mentioned above this system build for 3 different models, for this experiment. For,
YOLOv5 total of 3 phases were developed. For license plate detection, YOLOv5 was used in the
first step. Then, in the second level, segmentation was completed. And the last phase was about
the license plates’ identification of characters. Here, all the observations of these three phases are
illustrated. Same for Inception_ResNetv2 and VGG16 model. License plate detection using
YOLOv5 had an output confidence 81% prediction is correct. License plate detection using
Inception-ResNetv2 had an output confidence 61.95% prediction is correct for training dataset,
some time we get from their for other dataset 64%. License plate detection using VGG16 had an
output confidence 77.27% prediction is correct. After training with a lot of data, the test phase
showed us a better result for license plate detection. Images from different angles were used for
testing purpose. And our proposed model worked much better regarding those issues. Figure 3
and 4 show the detection of a license plate from an image. Yolov5 can detected bounding box in
small epoch, But Inception_ResNetv2 and VGG16 need high epoch. when we use low epochs in
our models the bounding box can’t detected perfectly. For Inception_ResNetv2 when we use
epoch 50 the bounding box is detect the model show in fig.14.
Fig.15.Bounding box and detected character by YOLOv5

21. fig.16. Bounding box and detected character by Inception-ResNetv2
fig.17.Bounding box and detected character by VGG16

22. fig.18.Bounding box and detected character by VGG16 using close image
For VGG16 when we use low or high epochs in our models the bounding box can’t detected
perfectly for Inception_ResNetv2 when we use epoch 50 the bounding box is detect the model
show in fig.12.
Fig.19.bounding box for VGG16 in small epochs (50)

23. Fig.20.bounding box box and character detection for VGG16 in small epochs (100)
Fig.21.bounding box and character detection for Inception-Resnetv2 in epochs(180),But low
regulation image
confidence rate (RR) is calculated as :
confidence= (No. of recognized samples /No. of total samples of that sign) × 100% [6]

24. model Character recognition time
YOLOv5 5.11s
Inception_ResNetv2 6.09s
VGG16 4.647s
Table5: Chracter recognition time for model
We can see from above table, using same character recognition model in those 3 object detection
model. We get different time for character recognition. VGG16 is fast for character recognition
from other two model
Fig.22.Chart of confidence of 3 model
We can see from the chart (fig.22) YOLOv5 model confidence is high then other 2
models. And it detect boundary box perfectly. But character recognition time is higher
than VGG16.But detecting correct character 100%.VGG16 confidence is 77.72. it is
higher than Inception_ResNetv2 model, but can not detect boundary box correctly.
Character recognition time is high. Inception_ResNetv2 model confidence is lower than
other two models but its can detected bounding box correctly. And character recognition
time is also lower than other.
So, YOLOv5 is best for detected bounding box for our experiment.
10. Stakeholders
Any individuals or groups that are affected, either positively or negatively, by a project,
initiative, policy, or organization are considered stakeholders. They may be internal or
external.
a. The stakeholders of our project are:
• Our Project Supervisor: Hasan Muhammad Kafi (Assistant Professor of
BAUST)
0
10
20
30
40
50
60
70
80
90
100
YOLOv5 Inception-ResNetv2 VGG16
Accuracy(%)
Accuracy(%)

25. b. Project Developers include:
• Most.Jannat-Ul-Ferdoush
• Mst.Habiba Hena Sumi
• Md. Talath Un Nabi
c. External Stakeholders:
• All the users and testers of our model.
11. Issues Encountered
1. In certain cases, the model may incorrectly recognize text, leading to inaccurate
results.
2. When training the model on a smaller dataset, it may struggle to accurately detect
number plates, as it has less data to learn from.
3. Training the model without a GPU can result in longer training sessions, as GPUs are
optimized for parallel processing and can significantly speed up training time.
12. Conclusion, Limitations and Future Recommendations
Limitations:
1. The model struggles to detect number plates in low-quality or noisy images.
2. There are instances where the model incorrectly recognizes text.
3. The model has difficulty detecting number plates in images taken from
complex angles.
4. When working with a small dataset, the model may have challenges accurately
detecting bounding boxes.
5. If the angle of the number plate is too high, all three models fail to detect the
bounding box during testing.
6. Images with significant noise pose challenges for the model to accurately detect
the bounding box.
7. Inception-ResNetv2 and VGG16 models have difficulty detecting bounding
boxes with low epochs, whereas YOLO performs better in this scenario.
Automatic Number Plate detection is a whole package of capturing the license plate from the
vehicle and to recognize it accurately which in bounding box. The purpose of this system is to
detect the Bengali license plate to identify the vehicles properly for different 3 model. Yolo5
confidence is 89%, Inception_ResNetv2 model confidence is 61.69%, VGG16 model confidence
is 77.27%. Our experimental best model is Yolo. If we analyze the 3 models, our experimental
best model is Yolo for bounding box detection. The ResNetv2 is less accurate than other towing
models, but it is also properly detected. And the last model was VGG16. The confidence was
good, but it did not properly detect the license plate character. Every system has limitation, any
one no work 100%. So, the system still has a large scope for further developments. Experimental
models for number plate region detection and plate extraction in the perspective of Bangladesh

26. the background scenes are more complex and also the weather of this country is always
changing. So, any one can develop those models for climate changing condition. If anyone can
remove limitation we can apply this project and can develop traffic violation and controlling
system.
References
[1]Shahed, Md Tanvir, et al. "Automatic Bengali number plate reader." Tencon 2017-2017
IEEE region 10 conference. IEEE, 2017.
[2]Kashyap, Abhishek, et al. "Automatic number plate recognition." 2018 international
conference on advances in computing, communication control and networking (ICACCCN).
IEEE, 2018.
[3]Saif, Nazmus, et al. "Automatic license plate recognition system for bangla license plates
using convolutional neural network." TENCON 2019-2019 IEEE Region 10 Conference
(TENCON). IEEE, 2019.
[4]Kakani, Bhavin V., Divyang Gandhi, and Sagar Jani. "Improved OCR based automatic
vehicle number plate recognition using features trained neural network." 2017 8th
international conference on computing, communication and networking technologies
(ICCCNT). IEEE, 2017.
[5] Sarif, Md Mesbah, et al. "Deep learning-based Bangladeshi license plate recognition
system." 2020 4th International Symposium on Multidisciplinary Studies and Innovative
Technologies (ISMSIT). IEEE, 2020.
[6] Al Nasim, Md Abdullah, et al. "An automated approach for the recognition of bengali
license plates." 2021 International Conference on Electronics, Communications and
Information Technology (ICECIT). IEEE, 2021.
[7] Joarder, Md Mahbubul Alam, et al. "Bangla automatic number plate recognition system
using artificial neural network." Asian Transactions on Science & Technology (ATST) 2.1
(2012): 1-10.
[8] Uddin, Md Azher, Joolekha Bibi Joolee, and Shayhan Ameen Chowdhury. "Bangladeshi
vehicle digital license plate recognition for metropolitan cities using support vector
machine." Proc. International Conference on Advanced Information and Communication
Technology. 2016.
[9] Suvon, Md Naimul Islam, Riasat Khan, and Mehebuba Ferdous. "Real time bangla
number plate recognition using computer vision and convolutional neural network." 2020
IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology
(IICAIET). IEEE, 2020
[10]Rahman, MM Shaifur, et al. "Bangla license plate recognition using convolutional neural
networks (CNN)." 2019 22nd International Conference on Computer and Information
Technology (ICCIT). IEEE, 2019.

27. [11] Islam, Tariqul, and Risul Islam Rasel. "Real-time bangla license plate recognition
system using faster r-cnn and ssd: A deep learning application." 2019 IEEE International
Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things
(RAAICON). IEEE, 2019.
[12] Sarif, Md Mesbah, et al. "Deep learning-based Bangladeshi license plate recognition
system." 2020 4th International Symposium on Multidisciplinary Studies and Innovative
Technologies (ISMSIT). IEEE, 2020.
Appendix
Attainment of Complex Engineering Problem (CP)
S.L. CP No. Attainment Remarks
1. P1: Depth of
Knowledge Required
K3 (Engineering Fundamentals):
K4 (Engineering Specialization):
K5 (Design):
K6 (Technology):
K8 (Research):
2. P2: Range of
Conflicting
Requirements
3. P3: Depth of Analysis
Required
4. P4: Familiarity of
Issues
5. P5: Extent of
Applicable Codes
6. P6: Extent of
Stakeholder
Involvement and
Conflicting
Requirements
7. P7: Interdependence
Mapping of Complex Engineering Activities (CA)
S.L. CA No. Attainment Remarks
1. A1: Range of
resources

28. 2. A2: Level of
interaction
3. A3:Innovation
4. A4:Consequences for
Society and the
Environment
5. A5: Familiarity