The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studi...The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.展开更多
Digit recognition from a natural scene text in video surveillance/broadcasting applications is a challenging research task due to blurred, font variations, twisted, and non-uniform color distribution issues with a dig...Digit recognition from a natural scene text in video surveillance/broadcasting applications is a challenging research task due to blurred, font variations, twisted, and non-uniform color distribution issues with a digit in a natural scene to be recognized. In this paper, to solve the digit number recognition problem, a principal-axis based topology contour descriptor with support vector machine （SVM） classification is proposed. The contributions of this paper include： a） a local descriptor with SVM classification for digit recognition, b） higher accuracy than the state-of-the art methods, and c） low computational power （0.03 second/digit recognition）, which make this method adoptable to real-time applications.展开更多
文摘The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.
基金supported by“MOST”under Grant No.105-2221-E-119-001
文摘Digit recognition from a natural scene text in video surveillance/broadcasting applications is a challenging research task due to blurred, font variations, twisted, and non-uniform color distribution issues with a digit in a natural scene to be recognized. In this paper, to solve the digit number recognition problem, a principal-axis based topology contour descriptor with support vector machine （SVM） classification is proposed. The contributions of this paper include： a） a local descriptor with SVM classification for digit recognition, b） higher accuracy than the state-of-the art methods, and c） low computational power （0.03 second/digit recognition）, which make this method adoptable to real-time applications.
