Traffic Sign Recognition System

The research group CAOS at the Computing Department of the Carlos III University of Madrid, Spain, offers an efficient recognition system for traffic signs using a set of classifiers. This system can be used as part of an active security system in cars. The fact that the system is based on a set of classifiers facilitates a distributed implementation, resulting in cheaper hardware and an improvement in fault-tolerance.Contrato Programa de Comercialización e Internacionalización. Sistema Regional de Investigación Científica e Innovación Tecnológica. (Comunidad de Madrid; Universidad Carlos III de Madrid

Smart Card Based Recognition System "Smart Health Care"

Person's medical record information is one factor that determines the quality ofservice provided by central health care services to patients and, therefore, thismedical record information should always be there when needed. Confidentiality ofmedical record information is essential arena this information to explain the specialrelationship between patient and physician, who must be protected from leakage inaccordance with the code of medical ethics and the applicable legislation.This research aims to design a medical record system in accordance with theprovisions of Indonesia's medical records, ensure data security, portability by theowner, maximize health services for the emergency condition, and accelerate andimprove health service outpatient at a hospital or reference between hospitals. Toachieve these objectives, the authors will study the government regulations regardingmedical records, medical records system to know some using existing smartcardcountryof other countries to obtain medical smartcard system is the safest amongthese systems

A robust braille recognition system

Braille is the most effective means of written communication between visually-impaired and sighted people. This paper describes a new system that recognizes Braille characters in scanned Braille document pages. Unlike most other approaches, an inexpensive flatbed scanner is used and the system requires minimal interaction with the user. A unique feature of this system is the use of context at different levels (from the pre-processing of the image through to the post-processing of the recognition results) to enhance robustness and, consequently, recognition results. Braille dots composing characters are identified on both single and double-sided documents of average quality with over 99% accuracy, while Braille characters are also correctly recognised in over 99% of documents of average quality (in both single and double-sided documents)

An Arabic Optical Braille Recognition System

Technology has shown great promise in providing access to textual information for visually impaired people. Optical Braille Recognition (OBR) allows people with visual impairments to read volumes of typewritten documents with the help of flatbed scanners and OBR software. This project looks at developing a system to recognize an image of embossed Arabic Braille and then convert it to text. It particularly aims to build fully functional Optical Arabic Braille Recognition system. It has two main tasks, first is to recognize printed Braille cells, and second is to convert them to regular text. Converting Braille to text is not simply a one to one mapping, because one cell may represent one symbol (alphabet letter, digit, or special character), two or more symbols, or part of a symbol. Moreover, multiple cells may represent a single symbol

Kannada Character Recognition System A Review

Intensive research has been done on optical character recognition ocr and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market, but most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of works on Indian language character recognition especially Kannada script among 12 major scripts in India. This paper presents a review of existing work on printed Kannada script and their results. The characteristics of Kannada script and Kannada Character Recognition System kcr are discussed in detail. Finally fusion at the classifier level is proposed to increase the recognition accuracy.Comment: 12 pages, 8 figure

Real-Time Face Recognition System Using KPCA, LBP and Support Vector Machine

With increasing security threats, Biometric systems have importance in different fields. This appears clearly exactly after the rapid development that happened in power of computing. In this paper, the Design and implementation of a real-time face recognition system are presented. In such a system, Kernel principal component analysis (KPCA) and Local binary pattern (LBP) are used as feature extraction methods with the aid of support vector machine (SVM) to work as a classifier. A comparison between traditional feature extraction methods as (PCA and LDA) and a proposal methods are performed as well as a comparison between support vector neural network and artificial neural network classifier are also implemented. Two types of experiments, On-line, and Off-line experiments are done. In the On-line experiment, a new database is created and used. While in the off-line experiment, two types of databases (ORL and YALE) are used to estimate the performance and efficiency of the system. The combinations of these methods together enhances the experimental results in compare with other methods

The Army word recognition system

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system

Iris Recognition System for Biometric Recognition

Volume 7 Issue 9 (September 201

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga