Operator context scanning to support high segmentation rates for real time license plate recognition

Giannoukos, Ioannis; Anagnostopoulos, Christos; Loumos, V.; Kayafas, E.

doi:10.1016/j.patcog.2010.06.008

Cited by 64 publications

(29 citation statements)

References 36 publications

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“…There is an extensive literature on text detection, but most of it are dedicated to specific contexts such as text detection in handwritten documents [11], text recognition in medieval manuscript images [12], and license plate recognition [13,14]. An exhaustive review of this work is far outside the scope of the paper, and the reader is referred to the survey of Sharma et al [15], that covers some advances in this area.…”

Section: Previous Workmentioning

confidence: 99%

T-HOG: An effective gradient-based descriptor for single line text regions

Minetto

Thome

Cord

et al. 2013

Pattern Recognition

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We discuss the use of histogram of oriented gradients (HOG) descriptors as an effective tool for text description and recognition. Specifically, we propose a HOG-based texture descriptor (T-HOG) that uses a partition of the image into overlapping horizontal cells with gradual boundaries, to characterize single-line texts in outdoor scenes. The input of our algorithm is a rectangular image presumed to contain a single line of text in Roman-like characters. The output is a relatively short descriptor, that provides an effective input to an SVM classifier. Extensive experiments show that the T-HOG is more accurate than Dalal and Triggs's original HOG-based classifier, for any descriptor size. In addition, we show that the T-HOG is an effective tool for text/non-text discrimination and can be used in various text detection applications. In particular, combining T-HOG with a permissive bottom-up text detector is shown to outperform state-of-the-art text detection systems in two major publicly available databases.

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Section: Previous Workmentioning

confidence: 99%

T-HOG: An effective gradient-based descriptor for single line text regions

Minetto

Thome

Cord

et al. 2013

Pattern Recognition

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“…25 fps). In [141], an operator context scanning (OCS) algorithm to accelerate the searching of the NP region within the HD image was proposed, and results have shown that the system is capable of processing 22 fps. When 144 compared with the proposed hardware-based NPL implementation, the proposed work can achieve more than 200 fps, and the proposed FPGA-based system can be used as a viable solution to replace software-based solutions where cost, size and energy consumption will be reduced.…”

Section: Proposed Approachmentioning

confidence: 99%

Automatic Number Plate Recognition on FPGA

Zhai

Bensaali

McDonald-Maier

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

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Automatic Number Plate Recognition on FPGA i AbstractIntelligent Transportation Systems (ITSs) play an important role in modern traffic management, which can be divided into intelligent infrastructure systems and intelligent vehicle systems. Automatic Number Plate Recognition systems (ANPRs) are one of infrastructure systems that allow users to track, identify and monitor moving vehicles by automatically extracting their number plates. ANPR is a well proven technology that is widely used throughout the world by both public and commercial organisations. There are a wide variety of commercial uses for the technology that include automatic congestion charge systems, access control and tracing of stolen cars. The fundamental requirements of an ANPR system are image capture using an ANPR camera and processing of the captured image. The image processing part, which is a computationally intensive task, includes three stages: Number Plate Localisation (NPL), Character Segmentation (CS) and Optical Character Recognition (OCR). The common hardware choice for its implementation is often high performance workstations. However, the cost, compactness and power issues that come with these solutions motivate the search for other platforms. Recent improvements in low-power high-performance Field Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs) for image processing have motivated researchers to consider them as a low cost solution for accelerating such computationally intensive tasks.Current ANPR systems generally use a separate camera and a stand-alone computer for processing. By optimising the ANPR algorithms to take specific advantages of technical features and innovations available within new FPGAs, such as low power consumption, development time, and vast on-chip resources, it will be possible to replace the high performance roadside computers with small in-camera dedicated platforms. In spite of this, costs associated with the computational resources required for complex algorithms together with limited memory have hindered the development of embedded vision platforms.The work described in this thesis is concerned with the development of a range of image The overall results achieved show that the entire ANPR system can be implemented on a single FPGA that can be placed within an ANPR camera housing to create a stand-alone unit. As the benefits of this are drastically improve energy efficiency and removing the need for the installation and cabling costs associated with bulky PCs situated in expensive, cooled, waterproof roadside cabinets. Certificate of OriginalityAutomatic Number Plate Recognition on FPGA iii Certificate of OriginalityI hereby certify that the work presented in this report is my original research and has not been presented for a higher degree at any other university or institute.

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“…Efficient algorithms can detect license plate location from car images. Their results show that the proposed methods are more effective (Ioannis et al, 2010;Danian et al, 2005;Nicolás et al, 2012).…”

Section: Introductionmentioning

confidence: 98%

Automatic Recognition System of Vehicle Plate for Recording Incoming Vehicles to the University Campus and the Outgoing

El-Harby¹,

Behery²,

Al-Dhawi³

2017

American Journal of Applied Sciences

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Automatic recognition of vehicle plate characters became a very important in our daily life because of the unlimited increase of vehicles that enter or leave a supervised area. This paper describes an automatic intelligent system that captures the images of vehicles and has the ability to recognize the plates of vehicles entering the university campus or leaving. A camera is built on the university gate for taking images of incoming or outgoing vehicles. A rear image of a vehicle is captured and processed. The system applies an intelligent filtering of the input image based on a set of filters removing unnecessary image elements preserving the position and shape of characters of the vehicle plate, histogram manipulation and edge detection techniques for plate localization and characters segmentation. Adaptive Neuro Fuzzy Inference System (ANFIS) is chosen as a classifier for recognizing the characters in the vehicle plates. This system can extracts the letters and numbers from vehicles plates. So, the extracted vehicle characters can be used to record the incoming vehicles to the university campus and the outgoing. The recognized vehicle characters are saved in the university database.

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Operator context scanning to support high segmentation rates for real time license plate recognition

Cited by 64 publications

References 36 publications

T-HOG: An effective gradient-based descriptor for single line text regions

T-HOG: An effective gradient-based descriptor for single line text regions

Automatic Number Plate Recognition on FPGA

Automatic Recognition System of Vehicle Plate for Recording Incoming Vehicles to the University Campus and the Outgoing

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