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Text Localization, Enhancement and Binarization in Multimedia Documents

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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Text Localization, Enhancement and Binarization in Multimedia Documents

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  1. 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

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