Reproduction of document collage; Juedische Winterhilfe der Juedische Gemeinde; Berlin Jewish Institutions

"auch 8000 Angestellte wurden geworben"

Parents as co-researchers at home: Using an observational method to document young children’s use of technology

This paper discusses the use of observational video recordings to document young children’s use of technology in their homes. Although observational research practices have been used for decades, often with video-based techniques, the participant group in this study (i.e., very young children) and the setting (i.e., private homes), provide a rich space for exploring the benefits and limitations of qualitative observation. The data gathered in this study point to a number of key decisions and issues that researchers must face in designing observational research, particularly where non-researchers (in this case, parents) act as surrogates for the researcher at the data collection stage. The involvement of parents and children as research videographers in the home resulted in very rich and detailed data about children’s use of technology in their daily lives. However, limitations noted in the dataset (e.g., image quality) provide important guidance for researchers developing projects using similar methods in future. The paper provides recommendations for future observational designs in similar settings and/or with similar participant groups.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

Block-based feature detection and matching for mosaicing of camera-captured document images

In this paper, we present a new feature-based approach for mosaicing of camera-captured document images. A novel block-based scheme is employed to ensure that corners can be reliably detected over a wide range of images. 2-D discrete cosine transform is computed for image blocks defined around each of the detected corners and a small subset of the coefficients is used as a feature vector A 2-pass feature matching is performed to establish point correspondences from which the homography relating the input images could be computed. The algorithm is tested on a number of complex document images casually taken from a hand-held camera yielding convincing results.

Edge-based connected component approach for skew correction of complex document images

Skew correction of complex document images is a difficult task. We propose an edge-based connected component approach for robust skew correction of documents with complex layout and content. The algorithm essentially consists of two steps - an 'initialization' step to determine the image orientation from the centroids of the connected components and a 'search' step to find the actual skew of the image. During initialization, we choose two different sets of points regularly spaced across the the image, one from the left to right and the other from top to bottom. The image orientation is determined from the slope between the two succesive nearest neighbors of each of the points in the chosen set. The search step finds succesive nearest neighbors that satisfy the parameters obtained in the initialization step. The final skew is determined from the slopes obtained in the 'search' step. Unlike other connected component based methods, the proposed method does not require any binarization step that generally precedes connected component analysis. The method works well for scanned documents with complex layout of any skew with a precision of 0.5 degrees.

Entropy based Skew correction of document images

The document images that are fed into an Optical Character Recognition system, might be skewed. This could be due to improper feeding of the document into the scanner or may be due to a faulty scanner. In this paper, we propose a skew detection and correction method for document images. We make use of the inherent randomness in the Horizontal Projection profiles of a text block image, as the skew of the image varies. The proposed algorithm has proved to be very robust and time efficient. The entire process takes less than a second on a 2.4 GHz Pentium IV PC.

Electronic Document Management in Construction – Research Issue and Results

Electronic document management (EDM) technology has the potential to enhance the information management in construction projects considerably, without radical changes to current practice. Over the past fifteen years this topic has been overshadowed by building product modelling in the construction IT research world, but at present EDM is quickly being introduced in practice, in particular in bigger projects. Often this is done in the form of third party services available over the World Wide Web. In the paper, a typology of research questions and methods is presented, which can be used to position the individual research efforts which are surveyed in the paper. Questions dealt with include: What features should EMD systems have? How much are they used? Are there benefits from use and how should these be measured? What are the barriers to wide-spread adoption? Which technical questions need to be solved? Is there scope for standardisation? How will the market for such systems evolve?

Use of Document Management Systems - a Case Study of the Finnish Construction Industry

Triggered by the very quick proliferation of Internet connectivity, electronic document management (EDM) systems are now rapidly being adopted for managing the documentation that is produced and exchanged in construction projects. Nevertheless there are still substantial barriers to the efficient use of such systems, mainly of a psychological nature and related to insufficient training. This paper presents the results of empirical studies carried out during 2002 concerning the current usage of EDM systems in the Finnish construction industry. The studies employed three different methods in order to provide a multifaceted view of the problem area, both on the industry and individual project level. In order to provide an accurate measurement of overall usage volume in the industry as a whole telephone interviews with key personnel from 100 randomly chosen construction projects were conducted. The interviews showed that while around 1/3 of big projects already have adopted the use of EDM, very few small projects have adopted this technology. The barriers to introduction were investigated through interviews with representatives for half a dozen of providers of systems and ASP-services. These interviews shed a lot of light on the dynamics of the market for this type of services and illustrated the diversity of business strategies adopted by vendors. In the final study log files from a project which had used an EDM system were analysed in order to determine usage patterns. The results illustrated that use is yet incomplete in coverage and that only a part of the individuals involved in the project used the system efficiently, either as information producers or consumers. The study also provided feedback on the usefulness of the log files.

Automatic text block separation in document images

Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-nomalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid. Our features are the HPP and its two transformed versions, namely, eigen and Fisher profiles. Four well known classifiers, namely, Nearest neighbor, Linear discriminant function, SVM's and artificial neural networks have been considered and efficiency of the combination of these classifiers with the above features is compared. A sequential floating feature selection technique has been adopted to enhance the efficiency of this separation task. The results give an average accuracy of about 96.

Document page layout analysis using Harris corner points

Extraction of text areas from the document images with complex content and layout is one of the challenging tasks. Few texture based techniques have already been proposed for extraction of such text blocks. Most of such techniques are greedy for computation time and hence are far from being realizable for real time implementation. In this work, we propose a modification to two of the existing texture based techniques to reduce the computation. This is accomplished with Harris corner detectors. The efficiency of these two textures based algorithms, one based on Gabor filters and other on log-polar wavelet signature, are compared. A combination of Gabor feature based texture classification performed on a smaller set of Harris corner detected points is observed to deliver the accuracy and efficiency.