Abstract

We present a language-independent optical character recognition system that is capable, in principle, of recognizing printed text from most of the world's languages. For each new language or script the system requires sample training data along with ground truth at the text-line level; there is no need to specify the location of either the lines or the words and characters. The system uses hidden Markov modeling technology to model each character. In addition to language independence, the technology enhances performance for degraded data, such as fax, by using unsupervised adaptation techniques. Thus far, we have demonstrated the language-independence of this approach for Arabic, English, and Chinese. Recognition results are presented in this paper, including results on faxed data.

Date

1999

Authors

Zhidong A Lu, Issam Bazzi, Andras Kornai, John Makhoul, Premkumar S Natarajan, Richard Schwartz

Conference

27th AIPR Workshop: Advances in Computer-Assisted Recognition

Volume

3584

Pages

96-104

Publisher

SPIE