An Automated Approach for the Recognition of Bengali License Plates

Nasim, Abdullah Al; Chowdhury, Atiqul Islam; Muna, Jannatun Naeem; Shah, Faisal Muhammad

doi:10.1109/icecit54077.2021.9641214

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their system achieved an impressive accuracy of 99.5% on 200 images and demonstrated real-time processing capabilities. Al Nasim et al [18] proposed a hybrid approach for ALPR focused on Bangladeshi vehicles, integrating the YOLO model for license plate detection and a CNN model for character recognition. This approach ensures accurate and automated license plate detection, addressing various applications such as tracking and billing systems.…”

Section: B Background Studymentioning

confidence: 99%

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Istiak,

Feng

2024

IJRASET

View full text Add to dashboard Cite

Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

show abstract

Section: B Background Studymentioning

confidence: 99%

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Istiak,

Feng

2024

IJRASET

View full text Add to dashboard Cite

show abstract

“…In their submission, (Al Nasim et al, 2021) proposed a method for Automatic Recognition of Bengali License Plates using a hybrid method for detecting license plates using characters from them. A capture input image is preprocesses and then YOLO object detection model was used to locate the position of the plate in the input image.…”

Section: Literaturementioning

confidence: 99%

“…This model will allow the vehicle's automated license plate detection system to avoid any misuse of it. The author reported the prediction accuracy of around 81% during testing (Al Nasim et al, 2021).…”

Section: Literaturementioning

confidence: 99%

A Real-Time Automatic Kurdistan Numberplate Recognition System

Ashir

2022

EAJSE

View full text Add to dashboard Cite

The current paper presents a real-time implementation of a numberplate recognition system for Kurdistan region of Iraq. Automatic numberplate recognition systems (ANPR) have played a key role in many places in enforcing regulations on safe driving and preventing vehicular theft, identity establishment and many other applications. Though there are number of such systems available, their accuracy and speed of execution at inference time is still a challenging issue. To detect a numberplate from a fast-moving vehicle or from a high-resolution camera input, an extremely fast algorithm is required capable of processing tenth of frames per second. This is a huge challenge for many systems and often a compromise is made between the accuracy of detection and execution speed. This work proposes and implement Automatic numberplate recognition system with high accuracy and capable of processing over 50 frames per second at image resolution of 1920x1080 on a raspberry pi B 4 processor. The proposed approach has two major parts: numberplate region localization and character recognition or extraction from the localized region. We used standard machine-learning approach to detect the region of interest using Haarlike features algorithm as feature extractor and Adaptive Boosting (AdaBoost) algorithm to train a cascade of weak learner's classifiers for classification. After detection of the numberplate region from the input image, an optical character recognition algorithm (Tesseract) is used to extract the characters from the image for display and other use. Tesseract is a machine-learning based OCR algorithm which was pretrained with many languages and made available by google. To increase the detection accuracy, we proposed a masked training approach. The masked training approach uses masked positive samples as negative samples during the training. We also investigated the effect of using different boosting optimization techniques on the overall accuracy of the system. The overall accuracy and inference speed has greatly been improved when tested on a raspberry pi 4 B hardware.

show abstract