Negative filter
automatic, extraction 17
(544.3 per million)
lr,34-3-N03-1018,ak
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from printed
<term>
text
</term>
. We
#2779We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction oftranslation lexicons from printed text.
measure(ment),20-3-N03-1018,ak
</term>
ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results
#2765We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reducecharacter and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
model,1-2-N03-1018,ak
</term>
of an
<term>
OCR system
</term>
. The
<term>
model
</term>
is designed for use in
<term>
error
#2714Themodel is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.
model,14-3-N03-1018,ak
finite-state models
</term>
, demonstrate the
<term>
model 's
</term>
ability to significantly reduce
<term>
#2759We present an implementation of the model based on finite-state models, demonstrate themodel 's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
model,6-3-N03-1018,ak
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
#2751We present an implementation of themodel based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
model,7-1-N03-1018,ak
systems . In this paper , we introduce a
<term>
generative probabilistic optical character recognition ( OCR ) model
</term>
that describes an end-to-end process
#2675In this paper, we introduce agenerative probabilistic optical character recognition ( OCR ) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
model,9-3-N03-1018,ak
implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model 's
</term>
#2754We present an implementation of the model based onfinite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
other,16-2-N03-1018,ak
on
<term>
post-processing
</term>
the
<term>
output
</term>
of
<term>
black-box OCR systems
</term>
#2729The model is designed for use in error correction, with a focus on post-processing theoutput of black-box OCR systems in order to make it more useful for NLP tasks.
other,23-1-N03-1018,ak
describes an end-to-end process in the
<term>
noisy channel framework
</term>
, progressing from
<term>
generation
#2691In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in thenoisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
other,38-1-N03-1018,ak
through its transformation into the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
. The
#2706In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into thenoisy output of an OCR system.
other,38-3-N03-1018,ak
translation lexicons
</term>
from printed
<term>
text
</term>
. We present an application of
<term>
#2783We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printedtext.
tech,14-2-N03-1018,ak
correction
</term>
, with a focus on
<term>
post-processing
</term>
the
<term>
output
</term>
of
<term>
black-box
#2727The model is designed for use in error correction, with a focus onpost-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.
tech,18-2-N03-1018,ak
post-processing
</term>
the
<term>
output
</term>
of
<term>
black-box OCR systems
</term>
in order to make it more useful for
#2731The model is designed for use in error correction, with a focus on post-processing the output ofblack-box OCR systems in order to make it more useful for NLP tasks.
tech,29-1-N03-1018,ak
framework
</term>
, progressing from
<term>
generation of true text
</term>
through its transformation into the
#2697In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing fromgeneration of true text through its transformation into the noisy output of an OCR system.
tech,29-2-N03-1018,ak
in order to make it more useful for
<term>
NLP tasks
</term>
. We present an implementation of
#2742The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful forNLP tasks.
tech,42-1-N03-1018,ak
the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
. The
<term>
model
</term>
is designed
#2710In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of anOCR system.
tech,7-2-N03-1018,ak
model
</term>
is designed for use in
<term>
error correction
</term>
, with a focus on
<term>
post-processing
#2720The model is designed for use inerror correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.