model,7-1-N03-1018,bq |
In this paper , we introduce a
<term>
generative probabilistic optical character recognition ( OCR ) model
</term>
that describes an end-to-end process in the
<term>
noisy channel framework
</term>
, progressing from generation of
<term>
true text
</term>
through its transformation into the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
.
|
#2674
In 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. |
other,23-1-N03-1018,bq |
In this paper , we introduce a
<term>
generative probabilistic optical character recognition ( OCR ) model
</term>
that describes an end-to-end process in the
<term>
noisy channel framework
</term>
, progressing from generation of
<term>
true text
</term>
through its transformation into the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
.
|
#2690
In 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,31-1-N03-1018,bq |
In this paper , we introduce a
<term>
generative probabilistic optical character recognition ( OCR ) model
</term>
that describes an end-to-end process in the
<term>
noisy channel framework
</term>
, progressing from generation of
<term>
true text
</term>
through its transformation into the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
.
|
#2698
In 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 oftrue text through its transformation into the noisy output of an OCR system. |
other,38-1-N03-1018,bq |
In this paper , we introduce a
<term>
generative probabilistic optical character recognition ( OCR ) model
</term>
that describes an end-to-end process in the
<term>
noisy channel framework
</term>
, progressing from generation of
<term>
true text
</term>
through its transformation into the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
.
|
#2705
In 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. |
tech,42-1-N03-1018,bq |
In this paper , we introduce a
<term>
generative probabilistic optical character recognition ( OCR ) model
</term>
that describes an end-to-end process in the
<term>
noisy channel framework
</term>
, progressing from generation of
<term>
true text
</term>
through its transformation into the
<term>
noisy output
</term>
of an
<term>
OCR system
</term>
.
|
#2709
In 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. |
model,1-2-N03-1018,bq |
The
<term>
model
</term>
is designed for use in
<term>
error correction
</term>
, with a focus on
<term>
post-processing
</term>
the
<term>
output
</term>
of black-box
<term>
OCR systems
</term>
in order to make it more useful for
<term>
NLP tasks
</term>
.
|
#2713
Themodel 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. |
tech,7-2-N03-1018,bq |
The
<term>
model
</term>
is designed for use in
<term>
error correction
</term>
, with a focus on
<term>
post-processing
</term>
the
<term>
output
</term>
of black-box
<term>
OCR systems
</term>
in order to make it more useful for
<term>
NLP tasks
</term>
.
|
#2719
The 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. |
tech,14-2-N03-1018,bq |
The
<term>
model
</term>
is designed for use in
<term>
error correction
</term>
, with a focus on
<term>
post-processing
</term>
the
<term>
output
</term>
of black-box
<term>
OCR systems
</term>
in order to make it more useful for
<term>
NLP tasks
</term>
.
|
#2726
The 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. |
other,16-2-N03-1018,bq |
The
<term>
model
</term>
is designed for use in
<term>
error correction
</term>
, with a focus on
<term>
post-processing
</term>
the
<term>
output
</term>
of black-box
<term>
OCR systems
</term>
in order to make it more useful for
<term>
NLP tasks
</term>
.
|
#2728
The 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. |
model,6-3-N03-1018,bq |
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model
</term>
's ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results involving
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from
<term>
printed text
</term>
.
|
#2750
We 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. |
tech,9-3-N03-1018,bq |
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model
</term>
's ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results involving
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from
<term>
printed text
</term>
.
|
#2753
We 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. |
model,14-3-N03-1018,bq |
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model
</term>
's ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results involving
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from
<term>
printed text
</term>
.
|
#2758
We 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. |
measure(ment),20-3-N03-1018,bq |
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model
</term>
's ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results involving
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from
<term>
printed text
</term>
.
|
#2764
We 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. |
tech,31-3-N03-1018,bq |
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model
</term>
's ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results involving
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from
<term>
printed text
</term>
.
|
#2775
We 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 involvingautomatic extraction of translation lexicons from printed text. |
other,37-3-N03-1018,bq |
We present an implementation of the
<term>
model
</term>
based on
<term>
finite-state models
</term>
, demonstrate the
<term>
model
</term>
's ability to significantly reduce
<term>
character and word error rate
</term>
, and provide evaluation results involving
<term>
automatic extraction
</term>
of
<term>
translation lexicons
</term>
from
<term>
printed text
</term>
.
|
#2781
We 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 fromprinted text. |