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.

明

Jōyō · KANJIDIC grade 2 · 8 strokes

bright, light

菜

Jōyō · KANJIDIC grade 4 · 11 strokes

vegetable, side dish, greens

Find exact-kanji names →

Source rows containing both26

Distinct written forms20

Given-name readings11

Surname readings0

Adjacent 明菜 forms3

Adjacent 菜明 forms0

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

明喜菜

Akina

あきな

10 source-recorded spellings in this reading group contain both 明 and 菜Given name

明寿菜

Asuna

あすな

4 source-recorded spellings in this reading group contain both 明 and 菜Given name

明侑菜

Ayuna

あゆな

3 source-recorded spellings in this reading group contain both 明 and 菜Given name

明菜

Meina

めいな

2 source-recorded spellings in this reading group contain both 明 and 菜Given name

明衣菜

Aina

あいな

1 source-recorded spelling in this reading group contain both 明 and 菜Given name

明里菜

Arina

ありな

1 source-recorded spelling in this reading group contain both 明 and 菜Given name

明菜

Akana

あかな

1 source-recorded spelling in this reading group contain both 明 and 菜Given name

妃明菜

Himena

ひめな

1 source-recorded spelling in this reading group contain both 明 and 菜Given name

明菜

Akena

あけな

1 source-recorded spelling in this reading group contain both 明 and 菜Given name

沙明菜

Sakana

さかな

1 source-recorded spelling in this reading group contain both 明 and 菜Given name

明菜

Minna

みんな

1 source-recorded spelling in this reading group contain both 明 and 菜

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.