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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: 2016 Fifteenth Mexican International Conference On Artificial Intelligence (micai): Advances In Artificial Intelligence Título del capítulo: An unsupervised approach for automatic discovery of metadata in document images
Metadata; Maximally Stable Extremal Regions (MSER); Conditional Random Fields (CRF)
Resumen:
The visual information contained in documents provides a rich set of
features that can be exploited to increase its understanding. The
typography, design or lexical properties of text constitute the clues
that help us identify at a glance those data from other. In this paper,
we present a methodology to identify, extract and automatically classify
the metadata of the document covers. A problem associated with metadata
discovery is the processing of the original document format. We propose
the combination of two methods, maximally stable extremal regions (MSER)
for detecting text in cover images with complex background, and
conditional random fields (CRF) for logical labeling elements in the
document. We show a selected set of visual and linguistic features used
to train our model. As a necessary proof of concept we incorporated the
methods in a desktop application and we executed some interesting
examples. Preliminary results show a performance improvement in text
recognition regarding traditional methods of metadata extraction for
document images. In particular, a problem that we seek to solve is the
ambiguity between the book title and the author.
