Corpus composition research

Kanji Pair Research

Enter one kanji to discover other characters recorded in the same JMnedict name spellings, or enter two kanji to inspect that exact co-occurrence. Every count in this research view describes the imported corpus—not population popularity in Japan.

麻

Jōyō · KANJIDIC grade 8 · 11 strokes

hemp, flax, numb

知

Jōyō · KANJIDIC grade 2 · 8 strokes

know, wisdom

Find exact-kanji names →

Source rows containing both11

Distinct written forms11

Given-name readings11

Surname readings0

Adjacent 麻知 forms7

Adjacent 知麻 forms2

Recorded names

Name readings containing both characters

The pair only has to occur in the same written form; the two kanji are not required to be adjacent unless counted in the adjacency statistics above.

Given name

知麻希

Chiaki

ちあき

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻知子

Machiko

まちこ

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻知

Machi

まち

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻知香

Machika

まちか

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻知会

Machie

まちえ

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻知代

Machiyo

まちよ

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

知麻里

Chimari

ちまり

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

小麻知

Komachi

こまち

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻亜知

Māchi

まあち

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻知音

Machine

まちね

1 source-recorded spelling in this reading group contain both 麻 and 知Given name

麻留知

Maruchi

まるち

1 source-recorded spelling in this reading group contain both 麻 and 知

Evidence boundary

Co-occurrence is not popularity or etymology

The pair relation comes from exact JMnedict source rows through the compact kanji-usage index. KANJIDIC2 contributes character metadata only. A high corpus count can reflect dictionary coverage and should not be read as a national usage frequency.