| 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 of true 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 the noisy output of an OCR system. |
| 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 involving automatic extraction of translation lexicons from printed text. |
