A New Approach for Overlay Text Detection and Extraction From Complex Video Scene

Cited 64 time in webofscience Cited 96 time in scopus
  • Hit : 462
  • Download : 1125
Overlay text brings important semantic clues in video content analysis such as video information retrieval and summarization, since the content of the scene or the editor's intention can be well represented by using inserted text. Most of the previous approaches to extracting overlay text from videos are based on low-level features, such as edge, color, and texture information. However, existing methods experience difficulties in handling texts with various contrasts or inserted in a complex background. In this paper, we propose a novel framework to detect and extract the overlay text from the video scene. Based on our observation that there exist transient colors between inserted text and its adjacent background, a transition map is first generated. Then candidate regions are extracted by a reshaping method and the overlay text regions are determined based on the occurrence of overlay text in each candidate. The detected overlay text regions are localized accurately using the projection of overlay text pixels in the transition map and the text extraction is finally conducted. The proposed method is robust to different character size, position, contrast, and color. It is also language independent. Overlay text region update between frames is also employed to reduce the processing time. Experiments are performed on diverse videos to confirm the efficiency of the proposed method.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2009-02
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON IMAGE PROCESSING, v.18, no.2, pp.401 - 411

ISSN
1057-7149
DOI
10.1109/TIP.2008.2008225
URI
http://hdl.handle.net/10203/12264
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
000262562600015.pdf(1.72 MB)Download
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 64 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0