| other,8-2-C94-1030,ak | order to judge three types of the <term> | errors | </term> , which are <term> characters </term> | #26534 In order to judge three types of theerrors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,46-2-C94-1030,ak | order Markov chain model </term> for <term> | Japanese kanji-kana characters | </term> and <term> English alphabets </term> | #26572 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model forJapanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,50-2-C94-1030,ak | Japanese kanji-kana characters </term> and <term> | English alphabets | </term> , assuming that <term> Markov probability | #26576 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters andEnglish alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,24-3-C94-1030,ak | well as correcting these errors in <term> | Japanese bunsetsu | </term> and <term> English words </term> . This | #26624 From the results of the experiments, it is concluded that the methods is useful for detecting as well as correcting these errors inJapanese bunsetsu and English words. | |
| other,64-2-C94-1030,ak | correct chain of <term> syllables </term> or <term> | kanji-kana characters | </term> is greater than that of erroneous | #26590 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables orkanji-kana characters is greater than that of erroneous chains. | |
| other,27-3-C94-1030,ak | in <term> Japanese bunsetsu </term> and <term> | English words | </term> . This paper describes the enhancements | #26627 From the results of the experiments, it is concluded that the methods is useful for detecting as well as correcting these errors in Japanese bunsetsu andEnglish words. | |
| other,62-2-C94-1030,ak | probability </term> of a correct chain of <term> | syllables | </term> or <term> kanji-kana characters </term> | #26588 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain ofsyllables or kanji-kana characters is greater than that of erroneous chains. | |
| tech,5-1-C94-1030,ak | optical character recognition </term> and <term> | continuous speech recognition | </term> of a <term> natural language </term> | #26503 In optical character recognition andcontinuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted. | |
| other,21-2-C94-1030,ak | substituted , deleted or inserted in a <term> | Japanese bunsetsu | </term> and an <term> English word </term> , | #26547 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in aJapanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| tech,40-2-C94-1030,ak | this paper proposes new methods using <term> | m-th order Markov chain model | </term> for <term> Japanese kanji-kana characters | #26566 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods usingm-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,19-1-C94-1030,ak | </term> , it has been difficult to detect <term> | error characters | </term> which are wrongly deleted and inserted | #26517 In optical character recognition and continuous speech recognition of a natural language, it has been difficult to detecterror characters which are wrongly deleted and inserted. | |
| other,25-2-C94-1030,ak | <term> Japanese bunsetsu </term> and an <term> | English word | </term> , and to correct these <term> errors | #26551 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and anEnglish word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,32-2-C94-1030,ak | word </term> , and to correct these <term> | errors | </term> , this paper proposes new methods | #26558 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct theseerrors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,10-1-C94-1030,ak | continuous speech recognition </term> of a <term> | natural language | </term> , it has been difficult to detect | #26508 In optical character recognition and continuous speech recognition of anatural language, it has been difficult to detect error characters which are wrongly deleted and inserted. | |
| tech,1-1-C94-1030,ak | <term> language families </term> . In <term> | optical character recognition | </term> and <term> continuous speech recognition | #26499 Inoptical character recognition and continuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted. | |
| measure(ment),55-2-C94-1030,ak | English alphabets </term> , assuming that <term> | Markov probability | </term> of a correct chain of <term> syllables | #26581 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming thatMarkov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| other,12-2-C94-1030,ak | the <term> errors </term> , which are <term> | characters | </term> wrongly substituted , deleted or | #26538 In order to judge three types of the errors, which arecharacters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. |
