| 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. |
