Multiple Chinese Vehicle License Plate Localization in Complex Scenes

Luo, Yiwen; Huang, Shaobing; Han, Fangjian

doi:10.1109/icivc.2018.8492857

Cited by 12 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further verify the performance of the proposed method, we compared our method on LPL and LPR performance with two state-of-the-art methods, those of Silva et al (12) and Luo et al (21). These two methods are representative methods with good recognition accuracy and speed, and have some similarities with the proposed method.…”

Section: Quantitative Comparisonmentioning

confidence: 96%

“…Although our method is slightly slower, it has potential advantages of accuracy. Luo et al (21) proposed a license plate detection and refinement method. It uses single multibox detector (SSD) to find license plate regions and then determines the corner points of those license plates after fitting their borderlines with their character contours by multilevel thresholding binarization.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Object Detection-Based License Plate Localization and Recognition in Complex Environments

Tao

Dong

Huang

et al. 2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Automatic license plate recognition (ALPR) has made great progress, yet is still challenged by various factors in the real world, such as blurred or occluded plates, skewed camera angles, bad weather, and so on. Therefore, we propose a method that uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents. In our method, YOLOv3-Tiny, an end-to-end object detection network, is used to locate license plate areas, and YOLOv3 to recognize license plate characters. According to the type and position of the recognized characters, a logical judgment is made to obtain the license plate number. We applied our method to a truck weighing system and constructed a dataset called SM-ALPR, encapsulating pictures captured by this system. It is demonstrated by experiment and by comparison with two other methods applied to this dataset that our method can locate 99.51% of license plate areas in the images and recognize 99.02% of the characters on the plates while maintaining a higher running speed. Specifically, our method exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.

show abstract

Section: Quantitative Comparisonmentioning

confidence: 96%

Section: Quantitative Comparisonmentioning

confidence: 99%

Object Detection-Based License Plate Localization and Recognition in Complex Environments

Tao

Dong

Huang

et al. 2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…In 2018, researchers on Chinese script used neural networks to recognize CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) recognition [159], Medical document recognition [160], License plate recognition [161] and text recognition in historical documents [162]. Researchers used Convolutional Neural Network(CNN) [159], [162], Convolutional Recurrent Neural Network(CRNN) [160] and Single Deep Neural Network(SDNN) [161] during these studies.…”

Section: Chinese Languagementioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

322

View full text Add to dashboard Cite

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

show abstract

“…In their findings, the overall segmentation accuracy reported was approximately 97%. Luo et al [24] proposed a license plate detection system for Chinese vehicles, where a single-shot multi-box detector was used for the detection method [25] and achieved 96.5% detection accuracy on their database.…”

Section: Detection or Localizationmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

View full text Add to dashboard Cite

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

show abstract

Multiple Chinese Vehicle License Plate Localization in Complex Scenes

Cited by 12 publications

References 7 publications

Object Detection-Based License Plate Localization and Recognition in Complex Environments

Object Detection-Based License Plate Localization and Recognition in Complex Environments

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Contact Info

Product

Resources

About