Sciweavers

Share
NAACL
2003

A Generative Probabilistic OCR Model for NLP Applications

8 years 11 months ago
A Generative Probabilistic OCR Model for NLP Applications
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. 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 for NLP tasks. We present an implementation of the model based on finitestate 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.
Okan Kolak, William J. Byrne, Philip Resnik
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Okan Kolak, William J. Byrne, Philip Resnik
Comments (0)
books