Purpose: In May 2012 UK visual standards for driving changed, in order to comply with European laws. Drivers need to have both a visual acuity of 6/12 AND be able to read a number plate at 20 metres. Previously the number plate test was the only visual acuity test. Methods: Four different distance visual acuity charts were used (Snellen, logMAR letter-similar to ETDRS, logMAR Landolt ring, distance reading acuity- similar to MNRead chart) and were presented at 6m. 120 drivers were tested binocularly without refractive correction. Participants were taken outside to perform the number plate test at 20m. A second study was conducted, with 38 participants whose vision was impaired to approximately 6/12 using simulation spectacles. Results: Differences between the visual acuities as measured by the charts were statistically but not clinically significant. For all charts there is an overlap zone within which participants may pass only one of the two tests, outside this range, participants pass or fail both tests. The 6/12 cut-off provides reasonable sensitivity and specificity for Snellen and logMAR letter charts. A poorer acuity cut-off was needed with the Landolt chart to maximize the relationship with the number plate test. Conclusions: The 6/12 visual cut-off and the number plate test will not always pass or fail the same drivers. Snellen and logMAR letter charts are recommended to be used to measure the visual acuity of drivers, but not Landolt rings. Fifteen percent of the sample could read a number plate at 20m, but was not able to achieve either 6/12 or +0.30 logMAR. The overlap zone is a helpful tool to identify those people who need advice from Eye Care Practitioners.

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Based on digital image processing techniques, the goal of this work is develop a method to automatically detect license plates. To achieve this goal, this thesis uses wavelet transform to first find the position of the license plate. A number of image processing techniques are then developed to identify each character on the license plate. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach, which is the followed by a simple conclusion.

The aim of this Master's thesis is designing and developing Android application for automatic number plate recognition with external database lookup. In the introduction we discuss possibilities of number plate recognition in general. Android platform fundamentals, necessary developer tools and multi-platform image processing library OpenCV are described in the second section. In the third section different database types and synchronization methods are introduced. In the fourth section we describe basics of image processing and different algorithms necessary for recognition. Application requirements, involving scene and hardware requirements are defined in the fifth section. In the sixth section application development and algorithm implementation is described. In the seventh section we evalute the results of the detection. In the last section results are summarized and goals are set for further improvement.

Intelligent 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 processing algorithms for NPL, CS and OCR and corresponding FPGA architectures. MATLAB implementations have been used as a proof of concept for the proposed algorithms prior to the hardware implementation. The proposed architectures are speed/area efficient architectures, which have been implemented and verified using the Mentor Graphics RC240 FPGA development board equipped with a 4M Gates Xilinx Virtex-4 LX40. The proposed NPL architecture can localise a number plate in 4.7 ms whilst achieving a 97.8% localisation rate and consuming only 33% of the available area of the Virtex-4 FPGA. The proposed CS architecture can segment the characters within a NP image in 0.2-1.4 ms with 97.7% successful segmentation rate and consumes only 11% of the Virtex-4 FPGA on-chip resources. The proposed OCR architecture can recognise a character in 0.7 ms with 97.3% successful recognition rate and consumes only 23% of the Virtex-4 FPGA available area. In addition to the three main stages, two pre-processing stages which consist of image binarisation, rotation and resizing are also proposed to link these stages together. These stages consume 9% of the available FPGA on-chip resources. 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.

In the presented master paper: Analysis of car number plate recognition, optical character recognition (OCR), OCR software, OCR devices and systems are reviewed. Image processing operators and artificial neural networks are presented. Analysis and application of image processing operators for detection of number plate is done. Experimental results of estimation of Kohonen and multilayer feedforward artificial neural network learning parameters are presented. Number plate recognition is performed by the use of multilayer feedforward artificial neural network. Model of number plate recognition system is created. Number plate recognition software works in Microsoft© Windows™ operating system. Software is written with C++ language. Experimental results of system model operation are presented.

The commercially used automated number plate recognition (ANPR) sys- tems constitute a mature technology which relies on dedicated industrial cam- eras capable of capturing high-quality still images. In contrast, the problem of ANPR from low-quality video sequences has been so far severely under- explored. This thesis proposes a trainable convolutional neural network (CNN) with a novel architecture which can efficiently recognize number plates from low-quality videos of arbitrary length. The proposed network is experimentally shown to outperform several existing approaches dealing with video-sequences, state-of-the-art commercial ANPR system as well as the human ability to recog- nize number plates from low-resolution images. The second contribution of the thesis is a semi-automatic pipeline which was used to create a novel database containing annotated sequences of challenging low-resolution number plate im- ages. The third contribution is a novel CNN based generator of super-resolution number plate images. The generator translates the input low-resolution image into its high-quality counterpart which preserves the structure of the input and depicts the same string which was previously predicted from a video-sequence. 1

Konceptet automatiserad igenkänning och avläsning av registreringsskyltarhar utvecklats mycket de senaste åren och användningen av Artificiellaneurala nätverk har introducerats i liten skala med lovande resultat. Viundersökte möjligheten att använda detta i ett automatiserat system förgarageportar och implementerade en prototyp för testning. Den traditionellaprocessen för att läsa av en skylt kräver flera steg, i vissa fall upp till fem.Dessa steg ger alla en felmarginal som aggregerat kan leda till över 30% riskför ett misslyckat resultat. I denna uppsats adresseras detta problem och medhjälp av att använda oss utav Artificiella neurala nätverk utvecklades enkortare process med endast två steg för att läsa en skylt, (1) lokaliseraregistreringsskylten (2) läsa karaktärerna på registreringsskylten. Dettaminskar antalet steg till hälften av den traditionella processen samt minskarrisken för fel med 13%. Vi gjorde en Litteraturstudie för att identifiera detlämpligaste neurala nätverket för uppgiften att lokalisera registreringsskyltarmed vår miljös begränsningar samt möjligheter i åtanke. Detta ledde tillanvändandet av Faster R-CNN, en algoritm som använder ett antal artificiellaneurala nätverk. Vi har använt metoden Design och Creation för att skapa enproof of concept prototyp som använder vårt föreslagna tillvägagångssätt föratt bevisa att det är möjligt att implementera detta i en verklig miljö. / The concept of automated recognition and reading of license plates haveevolved a lot the last years and the use of Artificial neural networks have beenintroduced in a small scale with promising results. We looked into thepossibility of using this in an automated garage port system and weimplemented a prototype for testing. The traditional process for reading alicense plate requires multiple steps, sometimes up to five. These steps all givea margin of error which aggregated sometimes leads to over 30% risk forfailure. In this paper we addressed this issue and with the help of a Artificialneural network. We developed a process with only two steps for the entireprocess of reading a license plate, (1) localize license plate (2) read thecharacters on the plate. This reduced the number of steps to half of theprevious number and also reduced the risk for errors with 13%. We performeda Literature Review to find the best suited algorithm for the task oflocalization of the license plate in our specific environment. We found FasterR-CNN, a algorithm which uses multiple artificial neural networks. We usedthe method Design and Creation to implement a proof of concept prototypeusing our approach which proved that this is possible to do in a realenvironment.