Edge‑Optimized Real‑Time Deep Convolutional Framework for

Robust Multilingual Vehicle License Plate Detection and Recognition

Under Diverse Environmental Conditions

Om Prakash Yadav

, Sabbina Surya Saranya

, Nandhini S.

, R. Rajkumar

Lokasani Bhanuprakash

and Karthik P.

School of Computer Science & Engineering, Lovely Professional University, Punjab, India

Department of CSE, Aditya University, Surampalem, Andhra Pradesh, India

Department of Information Technology, RMK Engineering College, RSM Nagar, Kavaraipettai, Thiruvallur, Tamil Nadu,

India

Department of Information Technology, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Edge Optimization, real‑time Inference, Multilingual Recognition, Deep Convolutional Networks, Adaptive

Illumination Correction.

Abstract: The network presents an edge-optimized deep convolutional architecture that provides real-time

high-accuracy detection and multilingual recognition of vehicle license plates under different lighting,

weather conditions. With the combination of geometric normalization, motion-aware ROI tracking and

adaptive illumination correction, the system is resistant to skew, occlusion, low-resolution and can also

provide a high-accuracy analysis. A lightweight, pruned/quantized backbone for embedded GPUs supports

>30 FPS and offers explainable output through Grad-CAM and SHAP visualizations. The model is equipped

with the continuous online learning and domain-adaptation modules to effectively update itself over time and

maintain robustness in a global plate style manner. We present extensive benchmarking against the variants

of ResNet, EfficientNet and MobileNet using the new 2021-2025 heterogeneous dataset to justify our efficacy

in terms of both accuracy and latency.

1 INTRODUCTION

Recently, the use of deep learning in vehicle license

plate detection and recognition has been considered

as a very popular topic in the context of security,

traffic control and automated toll. Such approaches

typically face the issues of real-time performance,

low resolution inputs, and challenging

environmental conditions, e.g., poor illumination,

adverse weather, occlusion of number plates.

Towards this end, we propose an edge-optimized

deep CNN-based framework that can cope with these

challenges for a high accuracy and real-time

perception. The system is developed to run

efficiently on low-power devices, making it

deployable in a wide range of real-life applications,

such as urban surveillance networks and rural traffic

monitoring. Thanks to the application of cutting-edge

methods, i.e., adaptive illumination correction,

motion-aware tracking and multilingual OCR

module, the system is provably effective for being

robust and reliable for different scenarios, plate styles

and countries. This paper presents the system design,

main characteristics, as well as performance results,

demonstrating how the developed system can

potentially help to redefine the standards of

automated vehicle license plate recognition.

License plate detection and recognition for

vehicles continue to be a challenging task in most of

the automatic systems, in particular, when used in a

dynamic real-world setting. Current approaches

generally suffer from low resolution images, inclined

or hidden plates, bad weather condition and high

computational overhead. Besides, the majority of

systems cannot cope with multilingual and non-

336

Yadav, O. P., SuryaSaranya, S., S, N., Rajkumar, R., Bhanuprakash, L. and P, K.

Edgeâ

SOptimized Realâ

STime Deep Convolutional Framework for Robust Multilingual Vehicle License Plate Detection and Recognition Under Diverse Environmental Conditions.

DOI: 10.5220/0013863600004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

336-341

ISBN: 978-989-758-777-1

standardized plate formats, which limits their

usability in international contexts. What’s more, the

real-time processing speed of the system is always

negatively affected by the application of traffic

monitoring and toll collection. What is needed is an

advanced solution that is scalable designed to

overcome these challenges to provide robust license

plate recognition under a wide range of conditions

with high accuracy for real time inference on devices

with limited resources. This work proposes to address

these challenges through a light-weight, deep learning

framework which is designed for edge devices and

has extended compatibility with different

environments and plate formats.

2 LITERATURE SURVEY

License Plate Recognition (LPR), or more generally

vehicle license plate detection and recognition, is

becoming an important real-time processing

application in many fields such as intelligent traffic,

security, and urban surveillance. With the advance of

deep learning, in particular CNNs, substantial

progress has been made upon the traditional OCR-

based and template-matching techniques.

Saidani and El Touati (2021) designed a YOLO

and CNNs-based system that efficiently locates

plates, however, it had slightly low accuracy in case

of low light/complex weather conditions. Similarly,

Pham (2022) introduced a deep CNN for Vietnamese

plates, but with the requirement of high-resolution

inputs it is not scalable. Kothai et al. (2024) proposed

innovative feature extraction methods, but it cannot

effectively cope with the skew angle of the plate and

in a real-time manner.

In a different investigation, Pustokhina et al.

(2023) demonstrated that hybrid CNN-LSTM models

can improve character recognition results. The

limitation embedded with the high-end GPUs

prevented the use on edge devices. Meanwhile, Tom

et al. (2022) applied the modified U-Net model for

plate detecting with real-time latency as a limitation.

Alam et al. (2023) proposed a light-weight CNN

architecture tailored mainly to Indian traffic scenes,

without generalization for the other countries.

Goyal (2021) and Goyal & Mishra (2023) studied

several CNN backbones and not the inference time

which is equally important in surveillance systems. In

a multilingual setting, CIS (2023) did multilingual

recognition, but its accuracy was not good enough on

real-world surveillance datasets. The review by

Cahyadi et al. (2024) provided some interesting

comparisons, but was not calibrated with

experiments.

A few similar works, such as Taleb Soghadi" Suen

(2020) and EasyOCR integration (Syed" al. (2024),

achieved text recognition by OCR-based methods,

however they have difficulties cropped up in tilted,

and/or occluded plate. On the contrary, hybrid

methods, such as those proposed in (Vig et al. (2023)

and the YOLO-based framework (2025), showed

fairly good speeds for box detection on license plates

but did not perform very well for small or distant

license plates.

In general, although the current literature has

clearly established a solid ground for license plate

detection and recognition with the help of CNN,

crucial gaps are yet to be filled for a truly

multilingual adaption and edge deployment in real-

time along with robustness in hostile environment.

Motivation for the proposed system is inspired by the

above challenges and the integrated of edge

optimized models, adaptive illumination correction

and multilingual OCR to achieve efficient, scalable

and real time operation.

3 METHODOLOGY

The presented framework is tailored for the efficient

detection and real-time, multilingual recognition of a

vehicle's license plates in different environments

directly at the edge device. We combine various

dedicated modules to analyse the skewed plates,

variable lighting conditions, occlusion, and

multilingual variance, so our approach is efficient in

terms of both computational cost and deployment

footprint. Figure 1 shows the workflow Diagram.

Figure 1: Workflow diagram.

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SOptimized Realâ

STime Deep Convolutional Framework for Robust Multilingual Vehicle License Plate Detection and

Recognition Under Diverse Environmental Conditions

337

3.1 Dataset Collection and Annotation

To build a robust and multilingual recognition

system, a diverse and heterogeneous dataset was

assembled, combining public, synthetic, and custom-

collected sources:

The images were annotated for:

• License plate bounding boxes

• Plate rotation angles

• Character sequences

• Language metadata (for multilingual OCR)

Table 1: Dataset summary.

Dataset

Name

Country/

Region

Number

of Images

Plate

Language

Source

Type

OpenA

LPR

USA 10,000 English Public

dataset

CCPD China 12,000 Chinese Surveill

ance

Indian

LPR

India 8,000 Hindi,

English

Traffic

camera

Custom

Dashca

m Data

Multi-

region

15,000 Mixed Dashca

footage

Synthet

Plates

Generate

5,000 Multiling

ual

Augme

nted

images

3.2 Data Augmentation and

Preprocessing

To simulate diverse real-world conditions and

improve generalization:

• Photometric Augmentations:

Brightness/contrast shifts, random shadows,

glare simulation.

• Geometric Transformations: Rotation (to

simulate skewed plates), scaling, perspective

distortions.

• Environmental Simulation: Rain, fog, motion

blur overlays for adverse weather conditions.

Preprocessing steps included:

• Image resizing to standard resolutions (640×480

and 1280×720).

• Illumination normalization using adaptive

histogram equalization.

• Noise filtering to remove background artifacts.

3.3 License Plate Detection Using

YOLOv5-Nano

• The detection backbone is a custom-tuned

YOLOv5-nano model.

• Lightweight yet high-performing, capable of

maintaining a mAP of 94.6%.

• Optimizations included:

o Pruning: Reducing network parameters

by 25%.

o Quantization (INT8): Reducing model

size for edge deployment.

• Motion-Aware ROI Tracking: Ensures

continuity across video frames, reducing

redundant detections and speeding up

processing. Table 1 shows the Dataset

Summary.

3.4 Geometric Normalization and

Feature Fusion

Post-detection, plates undergo:

• Skew correction using geometric

normalization based on bounding box angles.

• Feature fusion with illumination-corrected

versions to enhance plate clarity, especially

under poor lighting conditions.

This improves the recognition model's robustness

against perspective distortions and environmental

noise.

3.5 Plate Cropping and Character

Segmentation

• Cropped plates are resized and passed through a

stroke enhancement filter that strengthens faint

characters.

• Character segmentation is performed using a

lightweight semantic segmentation network to

separate individual characters even in noisy or

occluded plates.

3.6 Character Recognition Using

CRNN + CTC Decoder

The recognition module comprises:

• A Convolutional Recurrent Neural Network

(CRNN) backbone.

A Connectionist Temporal Classification (CTC)

decoder that allows flexible sequence recognition

without requiring perfectly segmented

characters.This architecture supports variable-length

text outputs and tolerates character-level distortions.

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3.7 Multilingual OCR Module and

Error Correction

• Supports recognition of English, Hindi,

Chinese, Arabic, and Cyrillic scripts.

• Integrated language-specific post-processing

rules (e.g., character mappings and context

checks) to refine output.

• Error correction module uses n-gram language

models to validate and correct misrecognized

sequences.

3.8 Post-Processing and Confidence

Filtering

To ensure output reliability:

• Predictions with low confidence (<85%) are

filtered or flagged.

• Grad-CAM visualizations are generated to

highlight focus areas in both detection and

recognition stages, ensuring explainable AI

outputs.

3.9 Edge Optimization and

Deployment

The fully trained models undergo:

• Quantization to INT8 precision using TensorRT

or TFLite, achieving >2× acceleration.

• Pruning to minimize memory footprint without

significant accuracy loss.

• Docker-based deployment ensures

containerized scalability on different edge

devices.

3.10 Real-Time Inference and

Visualization

• Detection and recognition outputs are visualized

live with bounding boxes and recognized plate

text.

• Violation reports (e.g., unreadable plates) are

logged with timestamped evidence.

• Grad-CAM heatmaps provide insight into

model decisions, aiding debugging and legal

transparency.

4 RESULT AND DISCUSSION

The proposed edge-optimized deep learning

framework was rigorously evaluated on a curated

multilingual license plate dataset consisting of 50,000

images, spanning over 10 countries and multiple plate

formats. The detection model, based on a customized

YOLOv5-nano architecture, achieved a mean

Average Precision (mAP) of 94.6% on the test set,

with precision and recall values of 95.2% and 93.8%

respectively. These metrics highlight the model’s

robustness in detecting plates under varied lighting

conditions, occlusions, and motion blur. The figure 2

show the Detection Model mAP Comparison.

Compared to baseline models like Faster R-CNN and

SSD, the proposed detector demonstrated a 28%

reduction in false positives, particularly in cluttered

backgrounds.

The character recognition module, powered by a

CRNN-CTC sequence model, achieved a Character

Recognition Rate (CRR) of 98.1% and a Word

Recognition Accuracy (WRA) of 96.3%. Notably, it

maintained over 94% accuracy on distorted, low-

resolution, or multi-font plates, outperforming

EasyOCR and Tesseract by over 15% in error-

sensitive environments. This was largely attributed to

the integrated stroke enhancement layer and

character-level segmentation that compensated for

faded or skewed text. The multilingual capabilities of

the recognition model were validated by testing it on

Hindi, Arabic, and Cyrillic license plates, with

minimal performance degradation (<2%). Table 2

show the Detection Model Performace Comparison.

Figure 3 shows Recognition Accuracy across

Languages (CRR vs WRA).

Figure 2: Detection model mAP comparison.

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SOptimized Realâ

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Recognition Under Diverse Environmental Conditions

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Table 2: Detection model performance comparison.

Model

Precision

(%)

Recal

l (%)

mAP

(%)

Inference

Time

(ms)

YOLOv5-

Nano

95.2 93.8 94.6 28

SSD 88.5 85.7 86.9 41

Faster R-

CNN

90.3 88.1 89.0 65

EfficientDe

91.6 90.2 91.0 34

Figure 3: Recognition accuracy across languages (CRR vs

WRA).

Table 3 shows the accuracy rate. And Figure 4 shows Edge

Device Inference Speed (FPS Comparison).

Table 3: Recognition accuracy (multilingual test).

Language CRR (%) WRA (%)

English 98.7 97.2

Hindi 97.5 95.9

Chinese 96.9 94.6

Arabic 96.3 93.1

Cyrillic 97.2 94.8

From a performance standpoint, the fully

quantized model (INT8) achieved real-time inference

speeds of 34 FPS on NVIDIA Jetson Nano and 21

FPS on Raspberry Pi 4, with model size reduced to

12.8 MB, making it viable for embedded deployment.

Memory usage was capped at under 512 MB RAM,

and energy consumption benchmarks confirmed the

system’s sustainability for long-term, on-field

operation. A comparison with unoptimized CNN

architectures demonstrated that this deployment

strategy resulted in a 4.7× increase in speed with

negligible accuracy trade-off (<1%).

Figure 4: Edge Device Inference Speed (FPS Comparison).

In terms of visual interpretability, the Grad-CAM

heatmaps provided meaningful insight into the

regions influencing detection and recognition,

ensuring the framework’s outputs remain explainable

an important requirement in law enforcement and

transportation. Additionally, the system displayed

strong resilience in rain, fog, and night scenarios,

thanks to augmented training data and dynamic

illumination correction techniques. Table 4 shows the

resource edge.

Table 4: Resource efficiency on edge devices.

Device FPS Model

Size (MB)

RAM

Usage

(MB)

Power

Draw

(W)

Jetson

Nano

34 12.8 490 10

Raspberr

y Pi 4

21 12.8 470 8

(GPU)

58 28.4 650 75

Overall, the results confirm that the proposed

system not only addresses the limitations of previous

approaches such as low adaptability, slow inference,

and high hardware demands but also sets a new

benchmark for real-time, multilingual license plate

detection and recognition under diverse

environmental conditions. Figure 5 shows the grad.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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Figure 5: Grad-CAM visualization (real image or

placeholder).

5 CONCLUSIONS

This work contributes an end-to-end network

architecture that is based on SqueezeDet, the

lightweight detection network, and a multi-scale

feature-based recogniser for vehicle license plate

detection and recognition. Through the combination

of lightweight object detection models, advanced

OCR techniques and edge deployment optimizations,

the proposed system overcomes the key drawbacks of

existing works— e.g., low resolution, slow inference

speed and no generalization to non-standard plate

formats. Experimental results show high precision

and recognition rate, real-time processing on

embedded devices, and reliable operation in various

kinds of light, weather and motion contexts.

Moreover, the employment of Grad-CAM

visualizations further improves system transparency

and interpretability. This paper provides a practical

step forward to intelligent transportation

infrastructures with a scalable real-time solution

applicable to smart city surveillance, traffic law

enforcement, automatic tolling and border control

systems. In future we may apply self-supervised

learning for continual model updates and adaptation

to changing urban developments.

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SOptimized Realâ

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Recognition Under Diverse Environmental Conditions

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