IR-LPR: A Large Scale Iranian License Plate Recognition Dataset

Rahmani, Mahdi; Sabaghian, Melika; Moghadami, Seyyedeh Mahila; Mohsen, Talaie, Mohammad; Naghibi, Mahdi; Keyvanrad, Mohammad Ali

doi:10.1109/iccke57176.2022.9960129

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…It is worth noting that this dataset treats license plate characters as separate objects and labels them individually, which makes creating a dataset a tedious task. IR-LPR [12] is a public dataset with 20967 car images and 27745 license plates available for CR tasks. Annotations are available for each character, not the entire license plate.…”

Section: Background Datasetsmentioning

confidence: 99%

Iranian License Plate Recognition Using a Reliable Deep Learning Approach

Hatami,

Jamali,

Sadedel

2024

Scientia Iranica

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The issue of Automatic License Plate Recognition (ALPR) has been a challenging one in recent years because of weather conditions, camera angle, lighting and different license plate characters. Due to advances in deep neural networks, it is now possible to recognize Iranian license plates using specific neural networks. The proposed method recognizes license plates in two steps. First, license plates are detected through the YOLOv4-tiny model, which is based on Convolutional Neural Network (CNN). Secondly, Convolutional Recurrent Neural Network (CRNN) and Connectionist Temporal Classification (CTC) are applied to recognize the license plate characters. For labels, one string of numbers and letters is enough without segmenting and labeling each separately. The proposed method boasts an average response time of 0.0074 seconds per image and 141 frames per second (fps) in the Darknet framework and 0.128 seconds per image in the TensorFlow framework for the License Plate Detection (LPD) part. This method has been proven to provide a highly accurate model with minimal storage space requirements, using less than 2MB for the Character Recognition (CR) model. There was an average accuracy of 75.14% and a response time of 0.435 seconds for the end-to-end process. The released code is available through GitHub.

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Section: Background Datasetsmentioning

confidence: 99%

Iranian License Plate Recognition Using a Reliable Deep Learning Approach

Hatami,

Jamali,

Sadedel

2024

Scientia Iranica

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“…Their system demonstrated an accuracy of 95.05% after testing over 5000 images. Another Iranian study [16] has compiled a complete dataset comprising 19,937 car images and 27,745 license plate characters, annotated with the entire license plate information. This dataset was experienced in license plate detection using several optimization Yolov5 and detectron2 frameworks.…”

Section: License Plate Recognition Systemmentioning

confidence: 99%

“…[37,38]. Subsequently, the captured vehicle is classified using the neural network framework [10][11][12][13][14][15][16] in object detection, vehicle classification, and plate localization within the frame. The better the vehicle classification, the more accurately the plate can be located within the image.…”

Section: Modelmentioning

confidence: 99%

Improving License Plate Identification in Morocco: Intelligent Region Segmentation Approach, Multi-Font and Multi-Condition Training

Laoula¹,

Midaoui²,

Youssfi³

et al. 2023

Adv. sci. technol. eng. syst. j.

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The exponential growth in the number of automobiles over the past few decades has created a pressing need for a robust license plate identification system that can perform effectively under various conditions. In Morocco, as in other regions, local authorities, public organizations, and private companies require a reliable License Plate Recognition (LPR) system that takes into account all plates specifications (HWP, VWP, DP, YP, and WWP) and multiple fonts used. This research paper introduces an intelligent LPR system implemented using the Yolov5 and Detectron2 frameworks, which have been trained on a customized dataset comprising multiple fonts (such as CRE, HSRP, FE-S, etc.) and accounting for different circumstances such as illumination, climate, and lighting conditions. The proposed model incorporates an intelligent region segmentation approach that adapts to the plate's type, thereby enhancing recognition accuracy and overcoming conventional issues related to plate separators. With the use of image preprocessing and temporal redundancy optimization, the model achieves a precision of 97,181% when handling problematic plates, including those with specific illumination patterns, separators, degradations, and other challenges, with little advantage to Yolov5 over Detecton2.

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