W96-0108 |
Post-Processing The architecture of the
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word correction
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system for OCR post-processing
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C96-2136 |
. Moreover , we t > el the
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word correction
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accuracy in Table 3 is satisfactory
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W14-3617 |
we describe two approaches for
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word correction
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. The first approach involves
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C96-2136 |
to resort to context-dependent
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word correction
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methods to overcome tile short
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N03-1018 |
first phase performs isolated
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word correction
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using rewrite rules , allowing
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W13-4418 |
accuracy probability of misspelled
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word correction
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. 3 Methods Chang et al. ( 2012
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W06-1648 |
relative probabilities that candidate
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word corrections
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would be observed . These probabilities
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C96-2136 |
words segmentation accuracy and
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word correction
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accuracy . The word segmen ration
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W14-6828 |
non-word correction and wrong
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word correction
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. In detail , stage one consists
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W15-5005 |
et al. , 2014 ) . 3.2 English
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Word Correction
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To improve translation of sentences
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C96-2136 |
apanese word segmentation and
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word correction
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. We will think of ' tile output
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W14-6828 |
relation matching based wrong
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word correction
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are the key techniques of our
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P07-1087 |
mark insertion , and misspelled
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word correction
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. In our email data , it corresponds
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P07-1087 |
computation consideration . Misspelled
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word correction
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can be done in the same framework
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C96-2136 |
regard less of orthography . For
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word correction
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accuracy , two tuples are equal
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W04-3009 |
lexical level , which is an isolated
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word correction
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prob - lem . However , errors
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W14-6828 |
to the results of Run2 , wrong
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word correction
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based on the knowledge of POS
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W14-6822 |
the preset word to compare the
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word correction
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which in database . We use the
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W96-0108 |
word-error correction . 2.4 The
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Word Correction
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System for OCR Post-Processing
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W14-1209 |
unknown words , context dependent
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word corrections
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are made . 5.1 Detecting abbreviations
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