Text Extraction, Enhancement and OCR in Digital Video

  • Huiping Li
  • David Doermann
  • Omid Kia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1655)

Abstract

In this paper we address the problem of text extraction, enhancement and recognition in digital video. Compared with optical character recognition (OCR) from document images, text extraction and recognition in digital video presents several new challenges. First, the text in video is often embedded in complex backgrounds, making text extraction and separation difficult. Second, image data contained in video frames is often digitized and/or subsampled at a much lower resolution than is typical for document images. As a result, most commercial OCR software can not recognize text extracted from video. We have implemented a hybrid wavelet/neural network segmenter to extract text regions and use a two stage enhancement scheme prior to recognition. First, we use Shannon interpolation to raise the image resolution, and second we postprocess the block with normal/inverse text classification and adaptive thresholding. Experimental results show that our text extraction scheme can extract both scene text and graphical text robustly and reasonable OCR results are achieved after enhancement.

Keywords

Linear Discriminant Analysis Video Frame Digital Video Document Image Text Block 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Huiping Li
    • 1
  • David Doermann
    • 1
  • Omid Kia
    • 2
  1. 1.Language and Media Processing Laboratory Institute for Advanced Computer StudiesUniversity of Maryland College Park
  2. 2.Advanced Network Technologies DivisionNational Institute of Standards and Technology Gaithersburg

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