A proposition of a robust system for historical document images indexation

08/28/2013 ∙ by Nizar Zaghden, et al. ∙ 0

Characterizing noisy or ancient documents is a challenging problem up to now. Many techniques have been done in order to effectuate feature extraction and image indexation for such documents. Global approaches are in general less robust and exact than local approaches. That's why, we propose in this paper, a hybrid system based on global approach(fractal dimension), and a local one based on SIFT descriptor. The Scale Invariant Feature Transform seems to do well with our application since it's rotation invariant and relatively robust to changing illumination.In the first step the calculation of fractal dimension is applied to images in order to eliminate images which have distant features than image request characteristics. Next, the SIFT is applied to show which images match well the request. However the average matching time using the hybrid approach is better than "fractal dimension" and "SIFT descriptor" if they are used alone.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

page 5

page 6

page 7

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.