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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.

Optical character recognition (OCR) software has advanced greatly in recent years. Machine-printed text can be scanned and converted to searchable text with word accuracy rates around 98%. Reasonably neat hand-printed text can be recognized with about 85% word accuracy. However, cursive handwriting still remains a challenge, with state-of-the-art performance still around 75%. Algorithms based on hidden Markov models have been only moderately successful, while recurrent neural networks have delivered the best results to date. This thesis explored the feasibility of using a special type of feedforward neural network to convert freeform cursive handwriting to searchable text. The hidden nodes in this network were grouped into clusters, with each cluster being trained to recognize a unique character bigram. The network was trained on writing samples that were pre-segmented and annotated. Post-processing was facilitated in part by using the network to identify overlapping bigrams that were then linked together to form words and sentences. With dictionary assisted post-processing, the network achieved word accuracy of 66.5% on a small, proprietary corpus. The contributions in this thesis are threefold: 1) the novel clustered architecture of the feed-forward neural network, 2) the development of an expanded set of observers combining image masks, modifiers, and feature characterizations, and 3) the use of overlapping bigrams as the textual working unit to assist in context analysis and reconstruction.

We present solution of steps necessary for binarization and text lines detection contained in printed documents digitized by the camera. We introduce a normalization of non-uniform illumination method for text photographs. We propose input bitmap binarization algorithm based on two-dimensional probability pixel model which also considers its surrounding. We continue with description of robust text lines orientation detector based on optimization of risk function using first order derivatives of image function. In the end we present text lines detection and segmentation algorithm. Final shape of segmented lines is optimized with usage of graph algorithm. Powered by TCPDF (www.tcpdf.org)

Master of Science / Department of Animal Sciences and Industry / Terry Houser / A total of 45 top sirloin butts (IMPS 184) were procured from three commercial beef processing facilities to determine the effects of post-mortem aging time and blade tenderization on the quality of beef gluteus medius (GM) steaks. Top sirloin butts were randomly assigned to five post-mortem aging periods (5, 19, 33, 47, and 61 days). One-half of each beef GM was randomly assigned to either a blade tenderized treatment or a non-blade tenderized treatment that was not blade tenderized. Steaks were then evaluated for thiobarbituric acid reactive substances (TBARS), metmyoglobin reducing activity (MRA), oxygen consumption rate (OCR), visual color panel, instrumental color, Warner-Bratzler shear force (WBSF), Lactic Acid Bacteria (LAB) enumeration, pH, and sensory properties. Aging × blade tenderization interactions (P < 0.05) were found for display color panel, discoloration panel, WBSF, overall tenderness, myofibrillar tenderness, bloody/serumy, metallic, overall sweet, and bitter (P < 0.05). As steaks were aged longer and blade tenderized they became more discolored during display and more tender. In addition, there were aging × display time interactions (P < 0.05) observed for L*, a*, b*, display color panel, and discoloration panel. As steaks were aged longer, they had increased L*, a*, b*, and hue angle values and display color panel scores when initially put into a retail case, but L*, a* and b* decreased and discoloration scores increased as display time increased. Furthermore, there were blade tenderization × display time interactions (P < 0.05) found for display and discoloration panels. Blade tenderized steaks discolored faster in retail display than non-blade tenderized steaks. With increased aging time, there was an increase (P < 0.05) in TBARS, OCR, initial color panel, LAB enumeration, and warmed-over flavor, as well as a decrease in MRA. Also, as aging increased there was a decrease (P < 0.05) in MRA, initial color panel scores, and WBSF values. Blade tenderization significantly increased (P < 0.05) initial color panel scores, rancid flavor, and spoiled flavor. Increasing the aging time of the GM, produced steaks with decreased color stability, altered the flavor profile, and increased tenderness. Blade tenderization significantly increased tenderness, increased discoloration in a retail case, and produced more undesirable flavors.

This thesis describes how we utilize machine learning and image preprocessing to create a system that can extract a license plate number by taking a picture of a car with an Android smartphone. This project was provided by ÅF at the behalf of one of their customers who wanted to make the workflow of their employees more efficient. The two main techniques of this project are object detection to detect license plates and optical character recognition to then read them. In between are several different image preprocessing techniques to make the images as readable as possible. These techniques mainly includes skewing and color distorting the image. The object detection consists of a convolutional neural network using the You Only Look Once technique, trained by us using Darkflow. When using our final product to read license plates of expected quality in our evaluation phase, we found that 94.8% of them were read correctly. Without our image preprocessing, this was reduced to only 7.95%.

Title: Obstacle course racing: the effect of obstacles on the total race time during a Spartan Race Objectives: The aim of this study was to determine the effect of 30-burpee punishment on subsequent running speed (SPD), heart rate (HR) following a missed obstacle, and the final race standings during a OCR race. Methods: In our thesis we used a method of analysis in an official Spartan Race in the Czech Republic. 11 male OCR competitors wore a GPS and HR monitor, from which HR and SPD were measured. Three specific obstacles were chosen where nearly all competitors did burpees (spear throw), half did burpees (traverse wall), and nearly none did burpees (monkey bars). The HR and SPD were measured during the entire race, and specifically noted when the competitors arrived at left the obstacle. Results: The average HR of the entire race was 166.45 ± 3.34 beats per minute. The average time spent at the successful obstacles without burpees was 23 ± 17 seconds, while the average time spent at the unsuccessful obstacles with burpees was 124 ± 11 seconds. The change in HR over the course of successful obstacles was 0.79 ± 3.69 beat per minute, while the change in HR during unsuccessful obstacles with burpees was -3.00 ± 6.70 beat per minute. Total time spent at each obstacle correlated with the final...

<p>This master’s thesis describes the work in creating a customised optical character recognition (OCR) application; intended for use in digitisation of theses submitted to the Uppsala University in the 18th and 19th centuries. For this purpose, an open source software called Gamera has been used for recognition and classification of the characters in the documents. The software provides specific algorithms for analysis of heritage documents and is designed to be used as a tool for creating domain-specific (i.e. customised) recognition applications.</p><p>By using the Gamera classifier training interface, classifier data was created which reflects the characters in the particular theses. The data can then be used in automatic recognition of ‘new’ characters, by loading it into one of Gamera’s classifiers. The output of Gamera are sets of classified glyphs (i.e. small images of characters), stored in an XML-based format.</p><p>However, as OCR typically involves translation of images of text into a machine-readable format, a complementary OCR-module was needed. For this purpose, an external Gamera module for page segmentation was modified and used.</p><p>In addition, a script for control of the OCR-process was created, which initiates the page segmentation on Gamera classified glyphs. The result is written to text files.</p><p>Finally, in a test for recognition accuracy, one of the theses was used for creation of training data and for test of data. The result from the test show an average accuracy rate of 82% and that there is a need for a better pre-processing module which removes more noise from the images, as well as recognises different character sizes in the images before they are run by the OCR-process.</p>

License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate localization algorithm is based on vertical edge features where constraints are used to filter out non-text regions. Vertical and horizontal projections are used for character segmentation and Multi Layered Perceptron (MLP) based Optical Character Recognition (OCR) module has been implemented for character identification. The extracted license plate characters are validated against possible license plate formats during the recognition process. The system is tested both with Turkish and foreign license plate images including various plate orientation, image quality and size. An accuracy of 92% is achieved for license plate localization and %88 for character segmentation and recognition.

Mit maschinellen Verfahren lässt sich die Qualität der Inhaltserschließung dramatisch steigern. intelligentCAPTURE ist seit 2002 produktiv im Einsatz in Bibliotheken und Dokumentationszentren. Zu dessen Verfahren gehören Module für die Dokumentenakquisition, insbesondere Scanning und OCR, korrekte Textextraktion aus PDF-Dateien und Websites sowie Spracherkennung für "textlose" Objekte. Zusätzliche Verfahren zur Informationsextraktion können optional folgen. Als relevant erkannter Content wird mittels der CAI-Engine (Computer Aided Indexing) maschinell inhaltlich ausgewertet. Dort findet ein Zusammenspiel computerlinguistischer Verfahren (sprachabhängige Morphologie, Syntaxanalyse, Statistik) und semantischer Strukturen (Klassifikationen, Systematiken, Thesauri, Topic Maps, RDF, semantische Netze) statt. Aufbereitete Inhalte und fertige, human editierbare Indexate werden schließlich über frei definierbare Exportformate an die jeweiligen Bibliothekssysteme und in der Regel auch an intelligentSEARCH übergeben. intelligentSEARCH ist eine zentrale Verbunddatenbank zum Austausch zwischen allen produktiven Partnern weltweit aus dem öffentlichen und privatwirtschaftlichen Bereich. Der Austausch ist auf tauschbare Medien, bislang Inhaltsverzeichnisse, aus urheberrechtlichen Gründen begrenzt. Gleichzeitig ist diese Datenbank "Open Content" für die akademische Öffentlichkeit mit besonders leistungsstarken Retrieval-Funktionen, insbesondere mit semantischen Recherche-Möglichkeiten und der Visualisierung von semantischen Strukturen (http://www.agi-imc.de/intelligentSEARCH.nsf). Sowohl für die Indexierung als auch für die Recherche können unterschiedliche semantische Strukturen genutzt werden - je nach Erkenntnisinteresse, Weltsicht oder Sprache.