Affiliations: [a] School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, China
| [b]
Key Lab of Modern Teaching Technology, Ministry of Education, Xi’an, Shaanxi, China
Correspondence:
[*]
Corresponding author. Junliang Ma, School of Computer Science, Shaanxi Normal University, No. 620, West Chang’an Avenue, Chang’an District, Xi’an 710119, Shaanxi, China. E-mail: junliangma@snnu.edu.cn.
Abstract: Video retrieval technology has drawn considerable attention over the years. Compared with the underlying information such as color, edge, etc., the text in the video contains rich semantic information and can well summarize the video information. Many scholars have proposed methods based on SVM to detect video text. For most of these methods, feature dimension is too large, and the time complexity and detection effect remain to be improved. In this paper, a new method of SVM video text detection based on color, edge and HOG features is proposed. And for the problem of single frame detection, the detection effect is improved based on the detection of three adjacent frames. In this paper, video text detection is implemented through steps such as sample selection, feature extraction, model training, and text detection. Finally, many experiments are performed to compare the proposed method with other literatures. The results show that the proposed single-frame and three-frame detection algorithm has a high recall rate and accuracy, which reduces the false detection rate and improves the effectiveness of video text detection.
Keywords: Video retrieval, text detection, support vector machine, feature extraction, classifier
