Image Segmentation for a Hierarchical and Scalable Model

Abstract

A novel color segmentation approach robust is against digitization noise and
adapted to contemporary document images. This is system scalable, hierarchical, versatile
and completely automated, i.e user independent. It proposes an adaptive
binarization/quantization without any penalizing information loss. This model may be
used for many purposes. For instance, we rely on it to carry out the first steps leading to
advertisement recognition in document images. Furthermore, the color segmentation
output is used to localize text areas and enhance optical character recognition (OCR)
performances. We held tests on a variety of magazine images to point up our contribution
to the well-known OCR product Abby Finer-Reader. We also get promising results with our
ad detection system on a large set of complex layout testing images.