Automatic License Plate Recognition System for Bangla License Plates using Convolutional Neural Network

Saif, Nazmus; Ahmmed, Nazir; Pasha, Sayem; Shahrin, Md. Saif Khan; Hasan, Md. Mahmudul; Islam, Salekul; Jameel, Abu Shafin Mohammad Mahdee

doi:10.1109/tencon.2019.8929280

Cited by 41 publications

(16 citation statements)

References 9 publications

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“…Furthermore, a similar system was proposed for Bangla number plate detection in [42], which showed a limitation of correctly recognizing the characters from large blur images. However, the proposed system could solve this issue by employing the super A system presented in [38] might have failed if the texture of the plate was not clear and the number plate region was short of the threshold of the projection operation.…”

Section: Accuracy (Acc) = Tp + Tn Tp + Tn + Fp + Fn (4)mentioning

confidence: 99%

“…Furthermore, a similar system was proposed for Bangla number plate detection in [42], which showed a limitation of correctly recognizing the characters from large blur images. However, the proposed system could solve this issue by employing the super resolution technique to get high-resolution images for easy detection.…”

Section: Accuracy (Acc) = Tp + Tn Tp + Tn + Fp + Fn (4)mentioning

confidence: 99%

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Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

et al. 2021

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Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

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Section: Accuracy (Acc) = Tp + Tn Tp + Tn + Fp + Fn (4)mentioning

confidence: 99%

“…Furthermore, a similar system was proposed for Bangla number plate detection in [42], which showed a limitation of correctly recognizing the characters from large blur images. However, the proposed system could solve this issue by employing the super resolution technique to get high-resolution images for easy detection.…”

Section: Accuracy (Acc) = Tp + Tn Tp + Tn + Fp + Fn (4)mentioning

confidence: 99%

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

et al. 2021

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“…BRTA has introduced the retro-reflective Bangla digital license plates in 2012. They have made it mandatory to use digital number plates in vehicles since 2016 (Saif et al, 2019). For this reason, we have considered digital number plates only in our research.…”

Section: Properties Of Bangladeshi Digital Number Platesmentioning

confidence: 99%

An Effective Method for the Recognition and Verification of Bangladeshi Vehicle Digital Number Plates

Islam¹,

Habib²

2021

Journal of Computer Science

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In this study, an effective and automatic technique has been proposed to detect, recognize and verify Bangladeshi number plates. There are four steps in the proposed method: Pre-processing, extraction and segmentation, recognition and verification of the number plate. Image contrast-enhancing and tilt correction techniques are used in the proposed method. Different techniques like edge detection, morphological operations, bounding box method and connected component analysis are used for the extraction and segmentation of number plates and their characters. The template matching method is used for recognizing the characters. The authentication and verification process for a vehicle is the distinctive feature of the proposed method. We have developed a dataset of 500 Bangladeshi number plate images for our research. A rich template dataset and a cloud database containing vehicle details have also been developed for number plate character recognition and vehicle verification purposes, respectively. The proposed method achieves detection, extraction, segmentation and recognition accuracies of 96.8, 94.8, 98.3 and 97.6%, respectively. It is observed that the proposed technique outperformed many other existing techniques.

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“…al. [27] leverages the advantage of YOLO in that it can detect and localize objects simultaneously in an image using a single CNN. They fed the cropped license plate images after detection to a YOLOv3 model for segmenting and recognizing the characters and numbers.…”

Section: License Plate Recognitionmentioning

confidence: 99%

Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications

Ashrafee¹,

Khan²,

Irbaz³

et al. 2021

Preprint

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Automatic License Plate Recognition systems aim to provide an end-to-end solution towards detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in lowresource environments. In our paper, we propose a novel two-stage detection pipeline paired with Vision API that aims to provide real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to identify multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP 0.5 score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).

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Automatic License Plate Recognition System for Bangla License Plates using Convolutional Neural Network

Cited by 41 publications

References 9 publications

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

An Effective Method for the Recognition and Verification of Bangladeshi Vehicle Digital Number Plates

Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications

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