Training-Free License Plate Detection Using Vehicle Symmetry and Simple Features

In this paper, we propose a training free license plate detection method. We use a challenging benchmark dataset for license plate detection. Unlike many existing approaches, the proposed approach is a training free method, which does not require supervised training procedure and yet can achieve a reasonably good performance. Our motivation comes from the fact that, although license plates are largely variant in color, size, aspect ratio, illumination condition and so on, the rear view of vehicles is mostly symmetric with regard to the vehicles central axis. In addition, license plates for most vehicles are usually located on or close to the vertical axis of the vehicle body along which the vehicle is nearly symmetric. Taking advantage of such prior knowledge, the license plate detection problem is made simpler compared to the conventional scanning window approach which not only requires a large number of scanning window locations, but also requires different parameter settings such as scanning window sizes, aspect ratios and so on

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%

Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate

MINHLP: Module to Identify New Hampshire License Plates

A license plate, referred to simply as a plate or vehicle registration plate, is a small plastic or metal plate attached to a motor vehicle for official identification purposes. Most governments require a registration plate to be attached to both the front and rear of a vehicle, although certain jurisdictions or vehicle types, such as motorcycles, require only one plate, which is usually attached to the rear of the vehicle. We present analysis of Automatic License Plate Recognition (ALPR) of New Hampshire (NH) plates using open source products. This thesis contains an implementation of a demonstrated model and analysis of the results. In this paper, OpenCV (computer vision library) and Tesseract (open source optical character reader) is presented as a core intelligent infrastructure. The thesis explains the mathematical principles and algorithms used for number plate detection, processes of proper characters segmentation, normalization and recognition. A description of the challenges involved in detecting and reading license plate in NH, previous studies done by others and the strategies adopted to solve them is also given

Embedded Dsp Based License Plate Localization

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmada sözü edilen ve tasarlanan plaka yer saptama uygulaması sayısal sinyal işleme tabanlı gömülü bir mimari üzerinde gerçek zamanlı görüntü işleme kıstaslarına uyularak oluşturulan, ilgi duyulan bir konudur ve trafik yönetimi, gümrük kontrolleri, otoyol ödeme sistemleri, çalıntı arabaların tanınması, park yerleri, yasak bölgelerin kontrolü gibi birçok uygulama alanında gerek duyulan tam işlevli ve özdevimli tanıma sistemlerinin ayırt edici bir özelliğidir. Komple tanıma sisteminin ayırt edici bir parçası olmasının nedeni bir kez plaka yeri doğru olarak saptandığında aslında sorunun tanıma aşamasına indirgenmesidir. Tanıma biriminin girişinde yer alması ile başarımındaki kazancı iyileştirme sorununun ötesinde, bilinen bilgisayar tabanlı sistemlerle karşılaştırıldığında uygulamasının işlevine göre dar hacimli, kolay taşınabilir, düşük enerji tüketimli, ve düşük maliyetli mimariye sahip bir görüntü gözetim sisteminin parçası olması öncelikli bir durumdur. İşlemsel süreçlerde, sistem tasarım ve geliştirme aşamalarında tüm bu kısıtlamaların göz önünde tutulması amaçlanmıştır.Yer saptama işlemsel süreci genel olarak ayrıt bulma, eşikleme, bağlı bileşen etiketleme, plaka karakterlerini saran dikdörtgenlerin, ve son olarak da giriş görüntüsündeki plaka yerinin belirlenmesi aşamalarından oluşur. Öngörüldüğü şekliyle, tümleşik devrenin kullanışlı, yetkin, karmaşık, ve çok işlevli paralel çalışan komutları ile yüksek başarımlı doğrudan bellek erişimi, genel amaçlı giriş, çıkış ve çok çekirdekli yapısından faydalınarak plaka yer saptama işlemsel sürecinin geliştirilmesine ek olarak, sayısal işaret işleme tabanlı gömülü gerçek zamanlı bir görüntü izleme sistemi tipik bilgisayar tabanlı sistemlerle kıyaslandığında hem başarım hem verimlilik açısından gereksinimleri oldukça karşılayacak şekilde tasarlanmış ve geliştirilmiştir.The system presented and designed in this work as an embedded DSP architecture corresponding to real time video processing constraints is an application of license plate localization which is a challenging issue and distinctive unit of full featured and considerably standardized automated recognition systems required in several application areas like traffic management, custom controls, toll-pay systems, identification of stolen cars, parking, controlling of restricted zones. The reason of the fact it is a distinctive part of overall recognition system is that the issue is basically reduced to a recognition stage once the location of the license plate is correctly found. Beyond the reason that it is an issue to enhance the performance gain as a very important milestone prior to recognition modules, it is a priority task as a part of typical video surveillance system that the application should propose compact design, portability, low power consumption and low cost architecture as compared with generic personal computer based systems. It is aimed to consider all these constraints in algorithm and system design and development. Localization Algorithm generally consists of edge detection, threshold, component labeling, determination of surrounding rectangles of plate characters candidates, and finally localization of the plate in an input image. As contemplated, a DSP based embedded real-time video surveillance system is designed and developed comparatively sufficient to generic computer based systems in resolutions of both performance and efficiency constraints in addition to license plate localization algorithm development by utilizing flexible, powerful, complex multifunction instructions, high performance direct memory access and general purpose input outputs and multi core structures of integrated DSP.Yüksek LisansM.Sc

High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development

Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices and using only a single low-volume sample (50–300 μL). However, upon the subsequent investigation of these models, only the glucose and lactate models appeared to be robust based upon their model coefficients containing the expected Raman vibrational signatures. On the downstream front, the HT Raman device was incorporated into the development of a cation exchange chromatography step for an Fc-fusion protein to compare different elution conditions. PLS models were derived from the spectra and were found to predict accurately monomer purity and concentration. The low molecular weight (LMW) and high molecular weight (HMW) species concentrations were found to be too low to be predicted accurately by the Raman device. However, the method enabled the classification of samples based on protein concentration and monomer purity, allowing a prioritisation and reduction in samples analysed using A280 UV absorbance and high-performance liquid chromatography (HPLC). The flexibility and highly configurable nature of this HT Raman spectroscopy microscope makes it an ideal tool for bioprocess research and development, and is a cost-effective solution based on its ability to support a large range of unit operations in both upstream and downstream process operations