A novel approach for segmentation of touching characters on the license plate

Wang, Ran; Wang, Guoyou; Liu, Jianguo; Tian, Jiangmin

doi:10.1117/12.2011233

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Since the license plate images might contain artifacts such as skew transform, shadows and blurring, generated during the image acquisition process, one of the most challenging tasks in ALPR is the character segmentation [Araújo et al, 2013;Soumya et al, 2014;Wang et al, 2013;Xing-lin and Yun-lou, 2012]. Araújo et al [2013] proposes a technique to segment characters using CCL and showed that the OCR results are greatly affected by the character segmentation step.…”

Section: License Plate Character Segmentationmentioning

confidence: 99%

“…The approach proposed by Soumya et al [2014] performed character segmenta-tion by counting the black pixels in the horizontal and vertical direction of each license plate region. Wang et al [2013] employed a sequence of techniques to improve the segmentation based on vertical projection and an A* pathfinding algorithm. In [Xinglin and Yun-lou, 2012], the authors proposed a technique to segment the characters using prior knowledge regarding the shape and the font used in the license plate considering license plates containing English (Latin) and Chinese characters.…”

Section: License Plate Character Segmentationmentioning

confidence: 99%

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License plate recognition based on temporal redundancy

Menotti

Schwartz

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Recognition of vehicle license plates is an important task applied to a myriad of real scenarios. Most approaches in the literature first detect an on-track vehicle, locate the license plate, perform a segmentation of its characters and then recognize the characters using an Optical Character Recognition (OCR) approach. However, these approaches focus on performing these tasks using only a single frame of each vehicle in the video. Therefore, such techniques might have their recognition rates reduced due to noise present in that particular frame. On the other hand, in this work we propose an approach to automatically detect the vehicle on the road and identify (locate/recognize) its license plate based on temporal redundant information instead of selecting a single frame to perform the recognition. We also propose two post-processing steps that can be employed to improve the accuracy of the system by querying a license plate database (e.g., the Department of Motor Vehicles database containing a list of all issued license plates and car models). Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points (p.p.) (an increase of 23.38%) of the baseline results, using our proposal temporal redundancy approach. Furthermore, additional 7.8 p.p. are achieved using the two post-processing approaches, leading to a final recognition rate of 89.6% on a dataset with 5, 200 frame images of 300 vehicles recorded at Federal University of Minas Gerais (UFMG). In addition, this work also proposes a novel benchmark, designed specifically to evaluate character segmentation techniques, composed of a dataset of 2, 000 Brazilian license plates (resulting in 14, 000 alphanumeric symbols) and an evaluation protocol considering a novel evaluation measure, the Jaccard-Centroid coefficient.

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Section: License Plate Character Segmentationmentioning

confidence: 99%

Section: License Plate Character Segmentationmentioning

confidence: 99%

License plate recognition based on temporal redundancy

Menotti

Schwartz

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…However, the license plates must be cropped automatically when applied on real applications, tackling the aforementioned problems. 15,16 Du et al 1 classify the license plate character segmentation techniques into five main categories:…”

Section: License Plate Character Segmentationmentioning

confidence: 99%

“…For instance, Wang et al 15 use two segmentation techniques in sequence and Chuang et al 21 applies super-resolution techniques.…”

Section: License Plate Character Segmentationmentioning

confidence: 99%

Benchmark for license plate character segmentation

Gonçalves

Silva

Menotti

et al. 2016

J. Electron. Imaging

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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.

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A two-stage character segmentation method for Chinese license plate

Tian

Wang

et al. 2015

Computers & Electrical Engineering

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A novel approach for segmentation of touching characters on the license plate

Cited by 6 publications

References 4 publications

License plate recognition based on temporal redundancy

License plate recognition based on temporal redundancy

Benchmark for license plate character segmentation

A two-stage character segmentation method for Chinese license plate

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