The technological developments seen in the last few decades have triggered a shift in how scholars in the humanities work when viewing historical documents in their repositories whenever research questions require an analysis of the original documents. Thanks to digitization, they are now able to work with digital facsimiles, so access to visual representations of historical documents has become much easier. The increasing amount of digital data available in virtual libraries, such as e-codices2 and manuscripta mediaevalia,3 provides new research possibilities, such as comparing digital facsimiles of different repositories and annotating digital images. In order to handle the data and to perform research tasks on digital facsimiles, scholars need usable tools. In addition to viewing facsimiles, direct searches for specific text passages in the digitized data are also needed. Furthermore, linking research data and annotations with corresponding text passages would be highly beneficial. Another desideratum is a tool for viewing all the samples of a specific text phrase, initial, or decoration contained in a document (or set of them). While tools for specific tasks4 do exist, none of them serve scholars in the humanities in all aspects of their work on digital facsimiles, i.e. the generation and presentation of item descriptions, content representation, and research data. Our vision is to realize DivaDesk, a VRE for scholars in the humanities that includes semi-automatic state-of-the-art methods from computer science. This undertaking requires an interdisciplinary approach and intensive collaboration between scholars in the humanities and computer science. On the one hand, DivaDesk should be appropriate for

In this paper, we present an efficient page segmentation method for historical document images. Many existing methods either rely on hand-crafted features or perform rather slow as they treat the problem as a pixel-level assignment problem. In order to create a feasible method for real applications, we propose to use superpixels as basic units of segmentation, and features are learned directly from pixels. An image is first oversegmented into superpixels with the simple linear iterative clustering (SLIC) algorithm. Then, each superpixel is represented by the features of its central pixel. The features are learned from pixel intensity values with stacked convolutional autoencoders in an unsupervised manner. A support vector machine (SVM) classifier is used to classify superpixels into four classes: periphery, background, text block, and decoration. Finally, the segmentation results are refined by a connected component based smoothing procedure. Experiments on three public datasets demonstrate that compared to our previous method, the proposed method is much faster and achieves comparable segmentation results. Additionally, much fewer pixels are used for classifier training.

Extracting candidate text connected components (CCs) is critical for CC-based text localization. Based on the observation that text strokes in born-digital images mostly have complete contours and the text pixels have high contrast with the adjacent non-text pixels, we propose a method to extract candidate text CCs by combining text contours and stroke interior regions. After segmenting the image into non-smooth and smooth regions based on local contrast, text contour pixels in non-smooth regions are detached from adjacent non-text pixels by local binarization. Then, obvious non-text contours can be removed according to the spatial relationship of text and non-text contours. While smooth regions include stroke interior regions and non-text smooth regions, some non-text smooth regions can be easily removed because they are not surrounded by candidate text contours. At last, candidate text contours and stroke interior regions are combined to generate candidate text CCs. The CCs undergo CC filtering, text line grouping and line classification to give the text localization result. Experimental results on the born-digital dataset of ICDAR2013 robust reading competition demonstrate the efficiency and superiority of the proposed method.

In this paper, we present a Conditional Random Field (CRF) model to deal with the problem of segmenting handwritten historical document images into different regions. We consider page segmentation as a pixel-labeling problem, i.e., each pixel is assigned to one of a set of labels. Features are learned from pixel intensity values with stacked convolutional autoencoders in an unsupervised manner. The features are used for the purpose of initial classification with a multilayer perceptron. Then a CRF model is introduced for modeling the local and contextual information jointly in order to improve the segmentation. For the purpose of decreasing the time complexity, we perform labeling at superpixel level. In the CRF model, graph nodes are represented by superpixels. The label of each pixel is determined by the label of the superpixel to which it belongs. Experiments on three public datasets demonstrate that, compared to previous methods, the proposed method achieves more accurate segmentation results and is much faster.

In this paper, we propose a new dataset and a ground-truthing methodology for layout analysis of historical documents with complex layouts. The dataset is based on a generic model for ground-truth presentation of the complex layout structure of historical documents. For the purpose of extracting uniformly the document contents, our model defines five types of regions of interest: page, text block, text line, decoration, and comment. Unconstrained polygons are used to outline the regions. A performance metric is proposed in order to evaluate various page segmentation methods based on this model. We have analysed four state-of-the-art ground-truthing tools: TRUVIZ, GEDI, WebGT, and Aletheia. From this analysis, we conceptualized and developed Divadia, a new tool that overcomes some of the drawbacks of these tools, targeting the simplicity and the efficiency of the layout ground truthing process on historical document images. With Divadia, we have created a new public dataset. This dataset contains 120 pages from three historical document image collections of different styles and is made freely available to the scientific community for historical document layout analysis research.

In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we apply convolutional autoencoders to learn features directly from pixel intensity values. Then, using these features to train an SVM, we achieve high quality segmentation without any assumption of specific topologies and shapes. Experiments on three public datasets demonstrate the effectiveness and superiority of the proposed approach.

Text localization in born-digital images is usually performed using methods designed for scene text images. Based on the observation that text strokes in born-digital images mostly have complete contours and the pixels on the contours have high contrast compared with the adjacent non-text pixels, we propose a method to extract candidate text components using local contrast. First, the image is segmented into smooth and non-smooth regions. After removing non-text smooth regions, the remaining smooth regions are merged with non-smooth regions to form a candidate text image, which is binarized into high-value and low-value connected components (CCs). The CCs undergo CC filtering, line grouping and line classification to give the text localization result. Experimental results on the born-digital dataset of ICDAR2013 robust reading competition demonstrate the efficiency and superiority of the proposed method.

Automatic layout analysis of historical documents has to cope with a large number of different scripts, writing supports, and digitalization qualities. Under these conditions, the design of robust features for machine learning is a highly challenging task. We use convolutional autoencoders to learn features from the images. In order to increase the classification accuracy and to reduce the feature dimension, in this paper we propose a novel feature selection method. The method cascades adapted versions of two conventional methods. Compared to three conventional methods and our previous work, the proposed method achieves a higher classification accuracy in most cases, while maintaining low feature dimension. In addition, we find that a significant number of autoencoder features are redundant or irrelevant for the classification, and we give our explanations. To the best of our knowledge, this paper is one of the first investigations in the field of image processing on the detection of redundancy and irrelevance of autoencoder features using feature selection.

We present a novel method for adding realistic degradations to historical document images in order to generate more training data. Degradation patches are extracted from other documents and applied to the target document in the gradient domain. Working in the gradient domain has not been done for this purpose in document images analysis so far. It has the advantage to prevent color inconsistencies and allows to efficiently avoid border effects. This paper contains the detailed description of our novel method, with a focus on the mathematical aspect of the transition to and from the gradient domain. Furthermore, we perform quantitative experiments where we investigate the effects of using synthetically generated training data on historical documents with different kind of degradations.

In this paper we propose a novel hybrid feature selection method for historical Document Image Analysis (DIA). Adapted greedy forward selection and genetic selection are used in a cascading way. We apply the proposed method to the task of historical document layout analysis on three handwritten datasets of diverse nature. The documents contain complex layouts, different handwriting styles, and several results of decay. The task is to segment each page into four areas: periphery, background, text block, and decoration. The proposed method selected significantly less features and resulted in significantly lower error rates than using all features. Compared to several conventional feature selection methods, the proposed method is competitive with respect to the number of selected features and the resultant error rates. In addition, we found that some features, e.g., Gradient, Laplacian, and local binary patterns (LBP), are selected by most of the feature selection methods and we give some explanations. This finding suggests a clue for the layout analysis on handwritten documents in general.

In this paper we present a novel text line segmentation method for historical manuscript images. We use a pyramidal approach where at the first level, pixels are classified into: text, background, decoration, and out of page, at the second level, text regions are split into text line and non text line. Color and texture features based on Local Binary Patterns and Gabor Dominant Orientation are used for classification. By applying a modified Fast Correlation-Based Filter feature selection algorithm, redundant and irrelevant features are removed. Finally, the text line segmentation results are refined by a smoothing post-processing procedure. Unlike other projection profile or connected components methods, the proposed algorithm does not use any script-specific knowledge and is applicable to color images. The proposed algorithm is evaluated on three historical manuscript image datasets of diverse nature and achieved an average precision of 91% and recall of 84%. Experiments also show that the proposed algorithm is robust with respect to changes of the writing style, page layout, and noise on the image.

In this paper we investigate the importance of individual features for the task of document layout analysis, in particular for the classification of the document pixels. The feature set consists of numerous state-of-the-art features, including color, gradient, and local binary patterns (LBP). To deal with the high dimensionality of the feature set, we propose a cascade of an adapted forward selection and a genetic selection. We have evaluated our feature selection method on three historical document datasets. For the classification we used machine learning methods which classify each pixel into either periphery, background, text block, or decoration. The proposed cascading feature selection method reduced the number of features significantly while preserving the cross-validation performance. Furthermore, it selected less features with comparable performance, compared with the conventional feature selection methods. In our analysis we found that LBP features are consistently selected by all feature selection methods on all three datasets. This indicates that LBP correlate highly with the pixel classes much more than any other type of features does. These findings suggest a clue in paradigm for document layout analysis in general.

We present the evaluation of a product identification task using the LIRe system and SURF (Speeded-Up Robust Features) for content-based image retrieval (CBIR). The evaluation is performed on the Fribourg Product Image Database (FPID) that contains more than 3'000 pictures of consumer products taken using mobile phone cameras in realistic conditions. Using the evaluation protocol proposed with FPID, we explore the performance of different preprocessing and feature extraction. We observe that by using SURF, we can improve significantly the performance on this task. Image resizing and Lucene indexing are used in order to speed up CBIR task with SURF. We also show the benefit of using simple preprocessing of the images such as a proportional cropping of the images. The experiments demonstrate the effectiveness of the proposed method for the product identification task.

In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Various color and texture features such as color variance, smoothness, Laplacian, Local Binary Patterns, and Gabor Dominant Orientation Histogram are used for classification. Some of these features have so far not got many attentions for document image layout analysis. By applying an Improved Fast Correlation-Based Filter feature selection algorithm, the redundant and irrelevant features are removed. Finally, the segmentation results are refined by a smoothing post-processing procedure. The proposed method is demonstrated by experiments conducted on three different historical handwritten document image datasets. Experiments show the benefit of combining various color and texture features for classification. The results also show the advantage of using a feature selection method to choose optimal feature subset. By applying the proposed method we achieve superior accuracy compared with earlier work on several datasets, e.g., We achieved 93% accuracy compared with 91% of the previous method on the Parzival dataset which contains about 100 million pixels.

We present in this paper a new database containing images of end-consumer products. The database currently contains more than 3'000 pictures of products taken exclusively using mobile phones. We focused the acquisition on 3 families of product: water bottles, chocolate and coffee. Nine mobile phones have been used and about 353 different products are available. Pictures are taken in real-life conditions, i.e. directly in the shops and without controlling the illumination, centering of the product or removing the background. Each image is provided with ground truth information including the product label, mobile phone brand and series as well as region of interest in the images. The database is made freely available for the scientific community and can be used for content-based image retrieval benchmark dataset or verification tasks.

The decompilation decode binary instructions and data into high level source code against compilation.It plays an important part in finding malware and discovering software vulnerability.But the recognition of template library functions is very difficult in decompilation of object-oriented language.This paper analyzes the existing algorithm of library function recognition and aims at the key problem of feature conflicts to introduce an improved algorithm based on feature-recognition.This algorithm can store much more library function informations to avoid feature conflicts and has been verified in some experiments about C++ STL recognition.

This paper intends to make a state of the art of design principles applicable in the ubiquitous computing (ubicomp) domain. Since design principles are well defined, accepted and used in Human-Computer interaction, it is not the case in ubicomp. We also analyze a concrete ubiquitous system project, establish a list of applied design principles and check if design principles for GUIs fit to this particular system.