| 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 | #2675 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,ak | describes an end-to-end process in the <term> | noisy channel framework | </term> , progressing from <term> generation | #2691 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,29-1-N03-1018,ak | framework </term> , progressing from <term> | generation of true text | </term> through its transformation into the | #2697 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 fromgeneration 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 | #2706 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,ak | the <term> noisy output </term> of an <term> | OCR system | </term> . The <term> model </term> is designed | #2710 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. | |
| tech,7-2-N03-1018,ak | model </term> is designed for use in <term> | error correction | </term> , with a focus on <term> post-processing | #2720 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,ak | correction </term> , with a focus on <term> | post-processing | </term> the <term> output </term> of <term> black-box | #2727 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. | |
| 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 | #2731 The 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,29-2-N03-1018,ak | in order to make it more useful for <term> | NLP tasks | </term> . We present an implementation of | #2742 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,9-3-N03-1018,ak | implementation of the <term> model </term> based on <term> | finite-state models | </term> , demonstrate the <term> model 's </term> | #2754 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,ak | finite-state models </term> , demonstrate the <term> | model 's | </term> ability to significantly reduce <term> | #2759 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,ak | </term> ability to significantly reduce <term> | character and word error rate | </term> , and provide evaluation results | #2765 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,ak | provide evaluation results involving <term> | automatic extraction | </term> of <term> translation lexicons </term> | #2776 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. | |
| lr,34-3-N03-1018,ak | <term> automatic extraction </term> of <term> | translation lexicons | </term> from printed <term> text </term> . We | #2779 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. |
