80,523 research outputs found
Baseline Detection in Historical Documents using Convolutional U-Nets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of historical documents.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
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various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
Profiling of OCR'ed Historical Texts Revisited
In the absence of ground truth it is not possible to automatically determine
the exact spectrum and occurrences of OCR errors in an OCR'ed text. Yet, for
interactive postcorrection of OCR'ed historical printings it is extremely
useful to have a statistical profile available that provides an estimate of
error classes with associated frequencies, and that points to conjectured
errors and suspicious tokens. The method introduced in Reffle (2013) computes
such a profile, combining lexica, pattern sets and advanced matching techniques
in a specialized Expectation Maximization (EM) procedure. Here we improve this
method in three respects: First, the method in Reffle (2013) is not adaptive:
user feedback obtained by actual postcorrection steps cannot be used to compute
refined profiles. We introduce a variant of the method that is open for
adaptivity, taking correction steps of the user into account. This leads to
higher precision with respect to recognition of erroneous OCR tokens. Second,
during postcorrection often new historical patterns are found. We show that
adding new historical patterns to the linguistic background resources leads to
a second kind of improvement, enabling even higher precision by telling
historical spellings apart from OCR errors. Third, the method in Reffle (2013)
does not make any active use of tokens that cannot be interpreted in the
underlying channel model. We show that adding these uninterpretable tokens to
the set of conjectured errors leads to a significant improvement of the recall
for error detection, at the same time improving precision
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