3,816 research outputs found
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
We present a new approach for recognition of complex graphic symbols in
technical documents. Graphic symbol recognition is a well known challenge in
the field of document image analysis and is at heart of most graphic
recognition systems. Our method uses structural approach for symbol
representation and statistical classifier for symbol recognition. In our system
we represent symbols by their graph based signatures: a graphic symbol is
vectorized and is converted to an attributed relational graph, which is used
for computing a feature vector for the symbol. This signature corresponds to
geometry and topology of the symbol. We learn a Bayesian network to encode
joint probability distribution of symbol signatures and use it in a supervised
learning scenario for graphic symbol recognition. We have evaluated our method
on synthetically deformed and degraded images of pre-segmented 2D architectural
and electronic symbols from GREC databases and have obtained encouraging
recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document
Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10,
1325-132
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
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