Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks

Abstract

Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multi-national and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%.

Recognition of vehicle country from license plate image based on Siamese network model with triplet loss function and negative sampling technique

“…Research on recognizing licensed plates from different countries has not yet been studied enough. A full-fledged study [26] with classification by Brazil, USA, Europe, Turkey, Saudi Arabia, and United Arab Emirates countries was carried out. The use of YOLOv2 [27] as an object detection model and VGG16 [3] as a classifier showed good performance on country recognition task.…”

License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

“…The first use of YOLO CNN was attempted by [18] to detect LPs of vastly different plate orientations, yielding 99.5% F1-score. YOLOv2 algorithm with modified ResNet50 CNN was proposed by [19] to localise and detect the nature of multi-national LP (country, size, and languages but did not work on recognising the characters on LPs), achieving 99.57% detection precision. [20] also used YOLOv2 because they claimed YOLOv3 has more layers that slow down the training, which is not entirely true depending on which CNN model to utilise in YOLO workflow.…”

“…Nevertheless, they also achieved 95 to 97.5% precision score with YOLOv2 algorithm. Similarly, [19] extended the dataset for multinational and multi-language LP, reaching 99.57% of AP in LP detection. [20] further enhance datasets by synthesising LPs to overcome small dataset size and train a custom CNN model ported to Fast-YOLO to perform ALPR.…”

Full depth CNN classifier for handwritten and license plate characters recognition

“…Park, Yoon & Park (2019) concerned USA and Korean LPs describing the problem as multi-style detection. CNN shrinkage-based architecture was studied in Salemdeeb & Erturk (2020) , utilizing the maximum number of convolutional layers that can be added. Salemdeeb & Erturk (2020) studied the LP detection and country classification problem for multinational and multi-language LPs from Turkey, Europe, USA, UAE and KSA, without studying CR problem.…”

“…CNN shrinkage-based architecture was studied in Salemdeeb & Erturk (2020) , utilizing the maximum number of convolutional layers that can be added. Salemdeeb & Erturk (2020) studied the LP detection and country classification problem for multinational and multi-language LPs from Turkey, Europe, USA, UAE and KSA, without studying CR problem. These researches studied LPs from 23 different countries where most of them use Latin characters to write the LP number, and totally five languages were concerned (English, Taiwanese, Korean, Chinese and Arabic).…”