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Query N03-1018 18 >
Shuffle 18 (839.2 per million)

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.
other,29-2-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> .	#2741 The 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.
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,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.
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.
lr,34-3-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> .	#2778 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 oftranslation 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.
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.
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,19-2-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> .	#2731 The model is designed for use in error correction, with a focus on post-processing the output of black-boxOCR systems in order to make it more useful for NLP tasks.
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,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.
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.
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.
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,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.
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.


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