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
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.
