We report on new form of multivariate granulometries based on rectangles of varying size and aspect ratio. These granulometries are used for describing visual similarity between document images. Rectangular granulometries are used to probe the layout structure of document images, and the rectangular size distributions derived from them are used as descriptors for each image. Feature selection i...

In this paper we present a hybrid approach to segment and classify contents of document images. A Document Image is segmented into three types of regions: Graphics, Text and Space. The image of a document is subdivided into blocks and for each block five GLCM (Grey Level Co-occurrence Matrix) features are extracted. Based on these features, blocks are then clustered into three groups using K-Me...

The aim of this document is to describe our methods used in the Medical Image Modality Classification and Ad-hoc Image Retrieval Tasks of ImageClef 2011. The main novelty in medical image modality classification this year was, that there were more classes (18 modalities) organized in a hierarchy and for some categories only few annotated examples were available. Therefore, our strategy in image...

Deep learning is a growing trend in computing. It is an improvement to artificial neural network. Deep Neural Networks are used in image classification, detection and segmentation. In this paper, an overview is carried out about the usage of deep neural network in various areas of image computing including image quality assessment, document imaging, object recognition, medical imaging, content ...

Author profiling is a text classification technique, which is used to predict the profiles of unknown text by analyzing their writing styles. Author profiles are the characteristics of the authors like gender, age, nativity language, country and educational background. The existing approaches for Author Profiling suffered from problems like high dimensionality of features and fail to capture th...

Classification is an important problem in image document processing and is often a preliminary step towards recognition, understanding, and information extraction. In this paper, the problem is formulated in the framework of concept learning and each category corresponds to the set of image documents with similar physical structure. We propose a solution based on two algorithmic ideas. First, w...

We describe a simple, fast, and accurate system for document image zone classification — an important subproblem of document image analysis — that results from a detailed analysis of different features. Using a novel combination of known algorithms, we achieve a very competitive error rate of 1.46% (n = 13811) in comparison to (Wang et al., 2006) who report an error rate of 1.55% (n = 24177) us...

We describe a simple, fast, and accurate system for document image zone classification — an important subproblem of document image analysis — that results from a detailed analysis of different features. Using a novel combination of known algorithms, we achieve a very competitive error rate of 1.46% (n = 13811) in comparison to (Wang et al., 2006) who report an error rate of 1.55% (n = 24177) us...

This paper deals with supervised document image classification. An original distance based strategy allows automatic feature selection. The computation of a distance between an image to be classified and a class representative (point of view) allows to estimate a membership function for all classes. The choice of the best point of view performs the feature selection. This idea is used by an alg...

The number of biomedical publications has increased noticeably in the last 30 years. Clinicians and medical researchers regularly have unmet information needs but require more time for searching than is usually available to find publications relevant to a clinical situation. The techniques described in this article are used to classify images from the biomedical open access literature into cate...