Artificial Mercosur license plates dataset

Silvano, Gilles; Silva, Ivanovitch; Ribeiro, Vinícius Campos Tinoco; Greati, Vitor Rodrigues; Bezerra, Aguinaldo; Endo, Patrícia Takako; Lynn, Theo

doi:10.1016/j.dib.2020.106554

Cited by 8 publications

(4 citation statements)

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“…Table 3 shows that the proposed approach outperformed the other methods and achieved the best scores for the IoU. To evaluate the recognition performance, the proposed method was compared with DALPR [34] and KLPR [43] using three datasets: the Pakistani license plate dataset (PLPD), artificial Mercosur license plates (AMLP) dataset [41], and Roboflow license plate dataset (RLPD) [42].…”

Section: Resultsmentioning

confidence: 99%

“…It is clear from the mentioned tables that the proposed method outperformed the other methods and achieved the best score. Note that the DALPR [34] method failed in the case of the AMLP [41] and RLPD [42] datasets because it is designed to handle license plates of fixed length, standard color, and standard format. The proposed method is more general and robust to variations in license plate styles and orientation.…”

Section: Resultsmentioning

confidence: 99%

“…The performance of the proposed approach is also compared with other methods using two publicly available datasets: artificial Mercosur license plates (AMLP) [41] and the Roboflow license plate dataset (RLPD) [42]. AMLP [41] and the RLPD [42] contain 3839 and 350 images with ground truth annotations, respectively.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

Yousaf¹,

Khan

Ali

et al. 2021

Sensors

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License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

Yousaf¹,

Khan

Ali

et al. 2021

Sensors

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“…Since collecting, labeling, and classifying visual data is a tough process and requires much effort and time, there has always been a pale interest among researchers to develop large-scale datasets for computer vision and machine learning. Although there are some standard datasets available for license plate detection [9][10][11][12] and character recognition [13][14][15], they cannot be employed to recognize Farsi characters in images.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Tourani

Soroori

Shahbahrami

et al. 2021

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

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Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.

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