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In optical character recognition and coni.inuous speech recognition of a natural language, it has been diflicult to detect error characters which are wrongly deleted and inserted. ]n <>rder to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese "bunsetsu" and an l';nglish word, and to correct these errors, this paper proposes new methods using rn-th order Markov chain model for Japanese "l~anjikana" characters and Fmglish alphabets, assuming that Markov l)robability of a correct chain of syllables or "kanji-kana" characters is greater than that of erroneous chains. From the results of the experiments, it is concluded that the methods is usefld for detecting as well as correcting these errors in Japanese "bunsetsu" and English words.

The "Selective Error Correction Method" to judge these three types of errors, and correct them, using ra-th order Markov chain model for Japanese 'kanji-kana' characters, has been proposed and shown to be useful to detect and correct errors generated randomly (Araki et al., 1994).In this paper, this method is applied to detect and correct erroneous characters in Japanese text input through an OCR.. The method is confirmed to be also elfective to detect and correct the errors introduced by the OCR.