Abstract

We describe several improvements that we have made in the BBN BYBLOS OCR System. First, we adopted continuous density hidden Markov models (HMMs) rather than discrete density HMMs. This resulted in improved accuracy when more training data is available. It also allowed us to use unsupervised speaker adaptation algorithms (borrowed from speech recognition) for adaptation to font, style, and quality. Second, we sped up the character recognition by a factor of about 50 so that a full page of 2,000 characters requires about 30 to 40 seconds for processing. Third, we tested the system on Chinese characters. This required development of tools tocreatea training corpus from available sources. It also required techniques for dealing with an open set of characters, where some of the characters may have no real training data. The end result was 1.2% character error on newspaper data.

Date

1999

Authors

Richard Schwartz Zhidong Lu, Prem Natarajan, Issam Bazzi, Andras Komai, John Makhoul

Journal

1999 Symposium on Document Image Understanding Technology

Pages

245