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Suppression of false alarms. m.... mean of the window s..... standard deviation of the window k..... parameter R.... dynamics of the gray values of the image M.... minimum gray value of the image. Text Localization, Enhancement and Binarization in Multimedia Documents.

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slide1

Suppression of false alarms

m.... mean of the window

s..... standard deviation of the window

k..... parameter

R.... dynamics of the gray values of the image

M.... minimum gray value of the image

Text Localization, Enhancement and Binarization in Multimedia Documents

Christian Wolf Jean-Michel Jolion Françoise Chassaing

A way to include more semantic knowledge into the process of indexing images and video is to use overlay or artificial text. It is rich in information but easy to use, e.g. by keyword based queries. We present an algorithm to localize artificial text in images and video sequences using a measure of accumulated gradients and morphological post processing. The quality of the localized text is enhanced by robust multiple frame integration. A new technique for the bina-rization of the text boxes based on maxi-mization of local contrast is proposed.

Text detection in video sequences

Initial frame integration (averaging)

Detection per

single frame

Tracking -

keeping track of

text occurrences

Suppression of

false alarms

Image Enhancement -

Multiple frame integration

Binarization

Text detection in a single frame

Multiple frame integration

Original Image

Robust bi-linear interpolation

Use the statis-tics to robustly create an inter-polated image for each frame image of the appearance.

Collect statistics on each pixel during its temporal appearance

Gradient calculation,

Accumulation

The pixels are weighted by their distance and an add-itional weight calculated from the temporal statistics:

time

Combine the images to get a single enhanced image.

Binarization

Otsu’s method, adapted to two thresholds.

Binarization

Niblack’s method and derived methods calculate a threshold for each pixel based on statistics from the pixels in a local window. We propose a method conceived for data found in multimedia documents. This data does not always correspond to the hypotheses taken by the traditional methods.

Niblack:

Mathematical morphology

- Noise removal

- Connection of characters to words

Sauvola et al.:

Verification of geometrical constraints,

Consideration of special cases,

Combinationof rectangles

The contrast of the window

Contrast in the cen-ter of the image

The maximum local contrast

Experimental Results

Result: Extracted text rectangles

Detection performance

Binarization examples

OCR/Binarization performance

Original image

Niblack

Sauvola et al.

Our method

C. Wolf: wolf@rfv.insa-lyon.fr http://rfv.insa-lyon.fr/~wolf J.-M. Jolion: jolion@rfv.insa-lyon.fr http://rfv.insa-lyon.fr/~jolion F. Chassaing: francoise.chassaing@francetelecom.fr