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.

• Type: Conference
• Year: 2003
• Where: NAACL
• Authors: Okan Kolak, William J. Byrne, Philip Resnik
• Keywords: Black-box Ocr Systems, NAACL 2003, NAACL 2007, Noisy Channel Framework, Ocr Systems