76,772 research outputs found
Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition
The global Information and Communications Technology (ICT) supply chain is a
complex network consisting of all types of participants. It is often formulated
as a Social Network to discuss the supply chain network's relations,
properties, and development in supply chain management. Information sharing
plays a crucial role in improving the efficiency of the supply chain, and
datasheets are the most common data format to describe e-component commodities
in the ICT supply chain because of human readability. However, with the surging
number of electronic documents, it has been far beyond the capacity of human
readers, and it is also challenging to process tabular data automatically
because of the complex table structures and heterogeneous layouts. Table
Structure Recognition (TSR) aims to represent tables with complex structures in
a machine-interpretable format so that the tabular data can be processed
automatically. In this paper, we formulate TSR as an object detection problem
and propose to generate an intuitive representation of a complex table
structure to enable structuring of the tabular data related to the commodities.
To cope with border-less and small layouts, we propose a cost-sensitive loss
function by considering the detection difficulty of each class. Besides, we
propose a novel anchor generation method using the character of tables that
columns in a table should share an identical height, and rows in a table should
share the same width. We implement our proposed method based on Faster-RCNN and
achieve 94.79% on mean Average Precision (AP), and consistently improve more
than 1.5% AP for different benchmark models.Comment: Globecom 202
Baseline Detection in Historical Documents using Convolutional U-Nets
Baseline detection is still a challenging task for heterogeneous collections
of historical documents. We present a novel approach to baseline extraction in
such settings, turning out the winning entry to the ICDAR 2017 Competition on
Baseline detection (cBAD). It utilizes deep convolutional nets (CNNs) for both,
the actual extraction of baselines, as well as for a simple form of layout
analysis in a pre-processing step. To the best of our knowledge it is the first
CNN-based system for baseline extraction applying a U-net architecture and
sliding window detection, profiting from a high local accuracy of the candidate
lines extracted. Final baseline post-processing complements our approach,
compensating for inaccuracies mainly due to missing context information during
sliding window detection. We experimentally evaluate the components of our
system individually on the cBAD dataset. Moreover, we investigate how it
generalizes to different data by means of the dataset used for the baseline
extraction task of the ICDAR 2017 Competition on Layout Analysis for
Challenging Medieval Manuscripts (HisDoc). A comparison with the results
reported for HisDoc shows that it also outperforms the contestants of the
latter.Comment: 6 pages, accepted to DAS 201
Relevance of ASR for the Automatic Generation of Keywords Suggestions for TV programs
Semantic access to multimedia content in audiovisual archives is to a large extent dependent on quantity and quality of the metadata, and particularly the content descriptions that are attached to the individual items. However, given the growing amount of materials that are being created on a daily basis and the digitization of existing analogue collections, the traditional manual annotation of collections puts heavy demands on resources, especially for large audiovisual archives. One way to address this challenge, is to introduce (semi) automatic annotation techniques for generating and/or enhancing metadata. The NWO funded CATCH-CHOICE project has investigated the extraction of keywords form textual resources related to the TV programs to be archived (context documents), in collaboration with the Dutch audiovisual archives, Sound and Vision. Besides the descriptions of the programs published by the broadcasters on their Websites, Automatic Speech Transcription (ASR) techniques from the CATCH-CHoral project, also provide textual resources that might be relevant for suggesting keywords. This paper investigates the suitability of ASR for generating such keywords, which we evaluate against manual annotations of the documents and against keywords automatically generated from context documents
XML Schema Clustering with Semantic and Hierarchical Similarity Measures
With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis
Heterogeneous data source integration for smart grid ecosystems based on metadata mining
The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-
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