  Most of the post-processing methods for character recognition rely on
contextual information of character and word-fragment levels. However, due to
linguistic characteristics of Korean, such low-level information alone is not
sufficient for high-quality character-recognition applications, and we need
much higher-level contextual information to improve the recognition results.
This paper presents a domain independent post-processing technique that
utilizes multi-level morphological, syntactic, and semantic information as well
as character-level information. The proposed post-processing system performs
three-level processing: candidate character-set selection, candidate eojeol
(Korean word) generation through morphological analysis, and final single
eojeol-sequence selection by linguistic evaluation. All the required linguistic
information and probabilities are automatically acquired from a statistical
corpus analysis. Experimental results demonstrate the effectiveness of our
method, yielding error correction rate of 80.46%, and improved recognition rate
of 95.53% from before-post-processing rate 71.2% for single best-solution
selection.
