Recognition Assistance: Treating Errors in Texts Acquired from Various Recognition Processes

Texts acquired from recognition sources—conti-nuous speech/handwriting recognition and OCR—generally have three types of errors re-gardless of the characteristics of the source in particular. The output of the recognition process may be (1) poorly segmented or not segmented at all; (2) containing underspecified symbols (where the recognition process can only indicate that the symbol belongs to a specific group), e.g. shape codes; (3) containing incorrectly identified sym-bols. The project presented in this paper addresses these errors by developing of a unified linguistic framework called the MorphoLogic Recognition Assistant that provides feedback and corrections for various recognition processes. The framework uses customized morpho-syntactic and syntactic analysis where the lexicons and their alphabets correspond to the symbol set acquired from the recognition process. The successful framework must provide three services: (1) proper disambi-guated segmentation, (2) disambiguation for un-derspecified symbols, (3) correction for incorr-ectly recognized symbols. The paper outlines the methods of morpho-syntactic and syntactic post-processing currently in use