A proposition of a robust system for historical document images indexation

TL;DR

This paper introduces a hybrid system combining fractal dimension and SIFT descriptors for robust indexing of historical document images, improving accuracy and matching time over individual methods.

Contribution

It proposes a novel hybrid approach that integrates global fractal analysis with local SIFT features for better document image indexation.

Findings

Hybrid approach improves matching accuracy.

Average matching time is reduced compared to individual methods.

Effective for noisy and ancient document images.

Abstract

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.