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International Journal of Computer Applications (0975 8887)

Volume 11 No.2, December 2010

10

A Proposition of a Robust System for Historical

Document Images Indexation

Nizar Zaghden1, 2, Remy Mullot2, Slim Kanoun1 and Adel M Alimi1

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 a 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 is 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 SIFT are used alone.

Keywords: historical documents, document

characterization, fractal dimension, SIFT descriptor, similarity measure.

1. INTRODUCTION

Nowadays a lot of information is still stored in libraries and great effort must be done to digitalize or extract features from the huge quantities of old documents. When talking about images containing mostly textual information, OCR systems can be applied to characterize image documents. But these Character Recognition Systems seems to fail when document images are ancients or even noisy. Many researches have been done to characterize old documents in different origins (latin, a The recognition of different classes in historical documents requires suitable techniques in order to identify similar classes. As contemporary documents, techniques dealing with global features can be applied to heterogeneous type of documents. But extracting local features from images differs from the language of the text written in documents. So, the application of methods based on local features may fail when it is applied to heterogeneous types of documents. We propose in this paper a new method based on both, global and local features (figure 1). This paper is organized as follows. Section 2 presents our image indexation approach in details. Section 3 reports the experimental results. Section 4 concludes the paper.

2. OUR IMAGE INDEXATION

APPROACH

We first introduce the phases which we followed in our approach. In fact, we have segmented manually about 1000 images issued from the CESR base with a resolution of 300 dpi each. The specificity of this base is that it is heterogeneous, and contains figures, different fonts. It deals with ancient documents, and as we know almost of techniques which suppose to have good results in

Contemporary documents may fail

Figure 1: global scheme of the proposed method

Image request Image base

Global features :

Fractal dimensions

Elimination of images

different from the image request

Local features :

SIFT descriptor

Extract images which

correspond well to the image request

1REGIM: Research Group on Intelligent

Machines,

University of Sfax, ENIS,

Department of Electrical Engineering,

BP W - 3038, Sfax, Tunisia

2L3I: Laboratoire Informatique Image

Interaction,

Université de La Rochelle,

France, BP 17042

International Journal of Computer Applications (0975 8887)

Volume 11 No.2, December 2010

11 Within this application, we are interested in textual content, and we apply the fractal dimension as a global approach in the first step. The global features of an image are often used by many researchers in the image retrieval domain. The global approach cannot represent image details or regions, particularly robustness to partial visibility and high informational content. For this, we propose in this work an hybrid approach combining both global and local features [6].

2.1 Fractal dimension

The fractal dimension is a useful method to quantify thequotesdbs_dbs3.pdfusesText_6