22,465 research outputs found
Text Segmentation Using Exponential Models
This paper introduces a new statistical approach to partitioning text
automatically into coherent segments. Our approach enlists both short-range and
long-range language models to help it sniff out likely sites of topic changes
in text. To aid its search, the system consults a set of simple lexical hints
it has learned to associate with the presence of boundaries through inspection
of a large corpus of annotated data. We also propose a new probabilistically
motivated error metric for use by the natural language processing and
information retrieval communities, intended to supersede precision and recall
for appraising segmentation algorithms. Qualitative assessment of our algorithm
as well as evaluation using this new metric demonstrate the effectiveness of
our approach in two very different domains, Wall Street Journal articles and
the TDT Corpus, a collection of newswire articles and broadcast news
transcripts.Comment: 12 pages, LaTeX source and postscript figures for EMNLP-2 pape
Estimation of the Handwritten Text Skew Based on Binary Moments
Binary moments represent one of the methods for the text skew estimation in binary images. It has been used widely for the skew identification of the printed text. However, the handwritten text consists of text objects, which are characterized with different skews. Hence, the method should be adapted for the handwritten text. This is achieved with the image splitting into separate text objects made by the bounding boxes. Obtained text objects represent the isolated binary objects. The application of the moment-based method to each binary object evaluates their local text skews. Due to the accuracy, estimated skew data can be used as an input to the algorithms for the text line segmentation
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
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