A Platform for Storing, Visualizing, and Interpreting Collections of Noisy Documents

International audienceThe goal of document image analysis is to produce interpretations that match those of a fluent and knowledgeable human when viewing the same input. Because computer vision techniques are not perfect, the text that results when processing scanned pages is frequently noisy. Building on previous work, we propose a new paradigm for handling the inevitable incomplete, partial, erroneous, or slightly orthogonal interpretations that commonly arise in document datasets. Starting from the observation that interpretations are dependent on application context or user viewpoint, we describe a platform now under development that is capable of managing multiple interpretations for a document and offers an unprecedented level of interaction so that users can freely build upon, extend, or correct existing interpretations. In this way, the system supports the creation of a continuously expanding and improving document analysis repository which can be used to support research in the field

An Approach for Curating Collections of Historical Documents with the Use of Topic Detection Technologies

Digital curation of materials available in large online repositories is required to enable the reuse of Cultural Heritage resources in specific activities like education or scientific research. The digitization of such valuable objects is an important task for making them accessible through digital platforms such as Europeana, therefore ensuring the success of transcription campaigns via the Transcribathon platform is highly important for this goal. Based on impact assessment results, people are more engaged in the transcription process if the content is more oriented to specific themes, such as First World War. Currently, efforts to group related documents into thematic collections are in general hand-crafted and due to the large ingestion of new material they are difficult to maintain and update. The current solutions based on text retrieval are not able to support the discovery of related content since the existing collections are multi-lingual and contain heterogeneous items like postcards, letters, journals, photographs etc. Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. To use existing topic detection technologies on Europeana collections there are several challenges to be addressed: (1) ensure representative and qualitative training data, (2) ensure the quality of the learned topics, and (3) efficient and scalable solutions for searching related content based on the automatically detected topics, and for suggesting the most relevant topics on new items. This paper describes in more details each such challenge and the proposed solutions thus offering a novel perspective on how digital curation practices can be enhanced with the help of machine learning technologies

Document Analysis Research in the Year 2021

The original publication is available at www.springerlink.comInternational audienceDespite tremendous advances in computer software and hardware, certain key aspects of experimental research in document analysis, and pattern recognition in general, have not changed much over the past 50 years. This paper describes a vision of the future where community-created and managed resources make possible fundamental changes in the way science is conducted in such fields. We also discuss current developments that are helping to lead us in this direction

An Attempt to Use Ontologies for Document Image Analysis

National audienceThis paper presents exploratory work on the use of semantics in Document Image Analysis. It is different than existing semantics-aware approaches in the sense that it approaches the problem from a a very domain specific angle, and tries to incorporate an open model based on a reduced ontology. As presented here, it consists of enhancing an existing platform for Document Image Analysis benchmarking using off-the-shelf tools. The platform on which it is based hosts a wide variety of image interpretation algorithms as well as a wide range of benchmarking data. These data are stored in a relational database, as well as their type definition, the association between data and algorithms, etc. This work tries to provide an experimental indication whether ontologies and automated reasoning can provide new or alternative ways to extract relations among different stored facts, or infer dependencies between various user-defined types, based on their interactions with algorithms and other types of data

Visual Analysis of High-Dimensional Point Clouds using Topological Abstraction

This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\''s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\''s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve

Interpretation, Evaluation and the Semantic Gap ... What if we Were on a Side-Track?

International audienceA significant amount of research in Document Image Analysis, and Machine Perception in general, relies on the extraction and analysis of signal cues with the goal of interpreting them into higher level information. This paper gives an overview on how this interpretation process is usually considered, and how the research communities proceed in evaluating existing approaches and methods developed for realizing these processes. Evaluation being an essential part to measuring the quality of research and assessing the progress of the state-of-the art, our work aims at showing that classical evaluation methods are not necessarily well suited for interpretation problems, or, at least, that they introduce a strong bias, not necessarily visible at first sight, and that new ways of comparing methods and measuring performance are necessary. It also shows that the infamous {\em Semantic Gap} seems to be an inherent and unavoidable part of the general interpretation process, especially when considered within the framework of traditional evaluation. The use of Formal Concept Analysis is put forward to leverage these limitations into a new tool to the analysis and comparison of interpretation contexts

Technology Forecasting Using Data Mining and Semantics: First Annual Report

The planning and management of research and development is a challenging process which is compounded by the large amounts of information which is available. The goal of this project is to mine science and technology databases for patterns and trends which facilitate the formation of research strategies. Examples of the types of information sources which we exploit are diverse and include academic journals, patents, blogs and news stories. The intended outputs of the project include growth forecasts for various technological sectors (with an emphasis on sustainable energy), an improved understanding of the underlying research landscape, as well as the identification of influential researchers or research groups. This paper focuses on the development of techniques to both organize and visualize the data in a way which reflects the semantic relationships between keywords. We studied the use of the joint term frequencies of pairs of keywords, as a means of characterizing this semantic relationship – this is based on the intuition that terms which frequently appear together are more likely to be closely related. Some of the results reported herein describe: (1) Using appropriate tools and methods, exploitable patterns and information can certainly be extracted from publicly available databases, (2) Adaptation of the Normalized Google Distance (NGD) formalism can provide measures of keyword distances that facilitate keyword clustering and hierarchical visualization, (3) Further adaptation of the NGD formalism can be used to provide an asymmetric measure of keyword distances to allow the automatic creation of a keyword taxonomy, and (4) Adaptation of the Latent Semantic Approach (LSA) can be used to identify concepts underlying collections of keywords

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Characterizing Water and Water-Related Energy Use in Multi-Unit Residential Structures with High Resolution Smart Metering Data

As urban populations continue to grow and expand, localized demands on water supplies continue to increase as well. These water supplies, which have been historically stable, are also threatened by an increasingly erratic climate. Together, these two factors have significantly increased the likelihood of long-term drought conditions in the American West. In response, water suppliers are investigating new ways to record water use in urban areas to better understand how water is used. One of these methods is smart meters; advanced devices that can record and transmit water use information directly to the water supplier. However, these devices can produce extremely large amounts of data, which can often be difficult to manage. This research investigated methods for data collection and management to advance the feasibility of larger smart meter networks. The techniques we developed are described, as well as how these techniques were used to estimate water and water-related energy use in several student dormitories on Utah State University’s campus. We also detail how water and water-related energy use were estimated. These results offer insight into how water and water-related energy are used in buildings like these, which may be of interest to water suppliers looking for ways to increase their understanding of water use beyond just the number of gallons used