Multimedia Tools and Applications

, Volume 69, Issue 1, pp 217–245 | Cite as

A framework for improved video text detection and recognition

Article

Abstract

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.

Keywords

Video OCR Video indexing Multimedia retrieval 

Notes

Acknowledgement

This work has been supported by the Mediaglobe project. Mediaglobe is a SME project of the THESEUS research program, supported by the German Federal Ministry of Economics and Technology on the basis of a decision by the German Bundestag (FKZ: 01MQ09031).

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Hasso-Plattner-Institute for IT-Systems EngineeringUniversity of PotsdamPotsdamGermany

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