Title
Text from corners: a novel approach to detect text and caption in videos.
Abstract
Detecting text and caption from videos is important and in great demand for video retrieval, annotation, indexing, and content analysis. In this paper, we present a corner based approach to detect text and caption from videos. This approach is inspired by the observation that there exist dense and orderly presences of corner points in characters, especially in text and caption. We use several discriminative features to describe the text regions formed by the corner points. The usage of these features is in a flexible manner, thus, can be adapted to different applications. Language independence is an important advantage of the proposed method. Moreover, based upon the text features, we further develop a novel algorithm to detect moving captions in videos. In the algorithm, the motion features, extracted by optical flow, are combined with text features to detect the moving caption patterns. The decision tree is adopted to learn the classification criteria. Experiments conducted on a large volume of real video shots demonstrate the efficiency and robustness of our proposed approaches and the real-world system. Our text and caption detection system was recently highlighted in a worldwide multimedia retrieval competition, Star Challenge, by achieving the superior performance with the top ranking.
Year
DOI
Venue
2011
10.1109/TIP.2010.2068553
IEEE Transactions on Image Processing
Keywords
Field
DocType
novel algorithm,corner point,text region,detect text,detecting text,text feature,important advantage,caption detection system,novel approach,caption pattern,detectors,edge detection,content analysis,optical imaging,harris corner detector,databases,feature extraction,decision tree,robustness,decision trees,optical flow,indexing
Computer vision,Decision tree,Corner detection,Pattern recognition,Computer science,Search engine indexing,Feature extraction,Robustness (computer science),Artificial intelligence,Motion estimation,Discriminative model,Optical flow
Journal
Volume
Issue
ISSN
20
3
1941-0042
Citations 
PageRank 
References 
55
1.37
23
Authors
6
Name
Order
Citations
PageRank
Xu Zhao135436.13
kaihsiang lin21205.66
Yun Fu34267208.09
Yuxiao Hu42209103.06
Yuncai Liu51234185.16
Thomas S. Huang6278152618.42