Towards an automatic vehicle access control system: License plate location

Mendes, Pedro; Neves, José; Tavares, Andréa Iabrudi; Menotti, David

doi:10.1109/icsmc.2011.6084108

Cited by 13 publications

(23 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the baseline approaches described in Section 2, our proposed technique described in Section 3.1 and this fourth baseline using prior-knowledge information, we perform three main evaluations: i) individual character segmentation (Section 4.2), ii) full license plate character segmentation to assess whether the approaches are able to segment all license plate characters(Section 4.3); and iii) optical character recognition on characters which were perfectly segmented and characters segmented using the baseline proposed in 7 (the baseline that achieved the best accuracy in our experiments) to assess its accuracy on both scenarios (Section 4.4). We performed the experiments using the dataset described in Section 3.2 3 .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Benchmark for license plate character segmentation

Gonçalves

Silva

Menotti

et al. 2016

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. Automatic License Plate Recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plates detection, segmention of license plate characters and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the License Plate Character Segmentation (LPCS) step, which effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a novel benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-Centroid coefficient, a new evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and their corresponding bounding box annotations. We also present a new straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on five LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, not every work performs all of these several steps. For instance, some of them [2][3][4] try to detect the license plates in the entire scene instead of detecting the vehicle first.…”

Section: Introductionmentioning

confidence: 99%

Benchmark for license plate character segmentation

Gonçalves

Silva

Menotti

et al. 2016

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…The edge-based technique contains many steps and this work is designed based on the work of Mendes et al [7]. The technique begins with converting RGB image to grey level image, given in Figure 3(a).…”

Section: Edge-based Processingmentioning

confidence: 99%

“…To extract the characters, many previous works presented edge-based method that was reported with high detection rate [5,6,7,8].…”

Section: Edge-based Processingmentioning

confidence: 99%

“…Then, Sobel's edge detection method is applied to extract vertical edges of an image. Next, morphological operations, opening and closing, are employed to manipulate the edges in order to create LP candidate regions [7], shown in Figure 3(c). After that, the image is converted to binary image by applying Otsu's method [10] for selecting automatic threshold, given in Figure 3(e).…”

Section: Edge-based Processingmentioning

confidence: 99%

See 1 more Smart Citation

License Plate Localization at Night

Boonsim¹

2017

IJCA

View full text Add to dashboard Cite

License plate localization is considered to be the significant process in Automatic Number Plate Recognition (ANPR) system, because the accuracy rate of license plate recognition relies on the performance of license plate localization. The majority of license plate localization papers are dedicated to daytime where many appearances can be used to locate license plate. The researches were also reported with high detection accuracy, more than 90%. However, a few studies are presented at night when license plate appearances are not easy to obtain. In this condition, license plate detection is very challenging due to the limitation of available appearances and other light sources may interfered. This paper presents a method to detect license plate position at night by combining color-based, edge-based and image processing techniques. The technique uses a variety of sizes of sub-image to improve local contrast in order to solve problems of low contrast and uneven-light images. The experiments were conducted on images at night in various lighting conditions and the method can detect license plate position with accuracy rate of about 85%.

show abstract

An Adaptive Vehicle License Plate Detection at Higher Matching Degree

Prates

Cámara-Chávez

Schwartz

et al. 2014

Advanced Information Systems Engineering

View full text Add to dashboard Cite

In this paper, a novel approach for vehicle license plate detection that improves in both efficiency and quality over the common multiscale search method is proposed. The detection efficiency is improved by employing the result of a single scale sliding window search as a promising guess of the license plate location. The quality is assured by locally refining the initial detection in multiple scales. The main benefit of our method is that we have reached a more precise detection with the analysis of 20 times fewer detection windows with high reliability (96% recall and 70% precision). We also compared our method with an edge-based hybrid approach.

show abstract

Towards an automatic vehicle access control system: License plate location

Cited by 13 publications

References 8 publications

Benchmark for license plate character segmentation

Benchmark for license plate character segmentation

License Plate Localization at Night

An Adaptive Vehicle License Plate Detection at Higher Matching Degree

Contact Info

Product

Resources

About