OcrV1, Main, Exploration, bibRecord, 001F33

Text Extraction, Enhancement and OCR in Digital Video

Identifieur interne : 001F33 ( Main/Exploration ); précédent : 001F32; suivant : 001F34

Text Extraction, Enhancement and OCR in Digital Video

Auteurs : Huiping Li [États-Unis] ; David Doermann [États-Unis] ; Omid Kia [États-Unis]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 1999.

RBID : ISTEX:EB8C6F62EFB36A70FC1A1334A7AD19CDE6B2FEB3

Abstract

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.

Url:

https://api.istex.fr/document/EB8C6F62EFB36A70FC1A1334A7AD19CDE6B2FEB3/fulltext/pdf

DOI: 10.1007/3-540-48172-9_29

Affiliations:

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to stream Main, to step Curation: 001F33

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Text Extraction, Enhancement and OCR in Digital Video

Text Extraction, Enhancement and OCR in Digital Video

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

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