other,31-1-N03-1018,bq |
</term>
, progressing from generation of
<term>
|
true text
|
</term>
through its transformation into
|
#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 of true text through its transformation into the noisy output of an OCR system. |
other,38-1-N03-1018,bq |
through its transformation into the
<term>
|
noisy output
|
</term>
of an
<term>
OCR system
</term>
. The
|
#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 the noisy output of an OCR system. |
tech,31-3-N03-1018,bq |
provide evaluation results involving
<term>
|
automatic extraction
|
</term>
of
<term>
translation lexicons
</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 involving automatic extraction of translation lexicons from printed text. |