Administrative document images are usually processed in black and white what generates many problems due to the errors related to the binarization. Besides all semantic information provided by the color is lost. Document images have a rich and highly variable content. The presence of false colors and artefacts introduced by the scanning and the compression alter the segmentation of the regions. Problems arise when there is no correspondence between the point clouds which are detected in a color space and the real regions of an image. In order to help the segmentation, we propose the extraction of the main colors of an image as a set of binary layers. Due to the industrial context, our approach has to run unsupervised on a generic dataset of color administrative documents. The originality of this approach is the use of a multiresolution analysis to detect the number of colors automatically. At a low resolution, a set of local regions is obtained thanks to a SLIC-based approach which takes into account the structure of documents and which combines both colorimetric information and spatial information. Then, a merging stage is applied on each resolution separately based on the colors which have been extracted at a lower resolution. This contribution can both feed the traditional process and exploit colorimetric information.

Degradation models are widely used to create large datasets with ground-truth for designing optimal denoising algorithms, and to assess the performance of different document analysis and recognition methods (OCR, segmentation, and so on). Since the processing of document images is usually performed in grayscale or in black and white, some degradation models have been proposed in order to simulate noise on these specific images. However, there is a growing interest in the study of color document images. In this context, there is a real need of tools able to simulate the effects of noise on color for an evaluation purpose. In this paper, we propose to extend a model from the state-of-the-art to color document images. Our model includes three main steps. First, seed-points are selected depending on color information. Then, they are associated to different kinds of noise in order to simulate the degradation effect occurring on different content. Last, the values of pixels located inside an elliptic region around the seed points are modified in order to obtain degraded regions.

This paper addresses the problem of color documents images segmentation in an industrial context. Automated Document Recognition (ADR) systems highly reduce time and resource costs of companies by managing their huge amount of administrative documents, and by optimizing their workflow. Most of the time, a binarization is performed due to their historical industrial process. Therefore, colorimetric information can improve the process. In this paper, we propose a hierarchical clustering based approach to extract dominant color masks of documents. Indeed, our dataset comprises different kind of scanned administrative document images such as invoices, forms, letters, and so on. We do not know a priori the number of dominant colors on our documents. These masks will further feed the inputs to an OCR in order to bring extra-information about the colorimetric context. This approach requires neither user interaction nor setting steps. Experiments on several types of documents show the relevance of the proposed approach