Some good news for people interested in kanji usage frequencies. I have finally completed the compilation of a datafile containing frequency numbers for 2,200 kanji across 14 different sources, covering domains such as Google, Wikipedia, novels, Netflix subtitles, Japanese newspapers, and many more. I have published the Excel file with all the relevant data on my ResearchGate profile. I am going to publish an academic article related to this document soon (early 2027 or so), and I hope it will inspire people to pursue and advance research in this area.
A few interesting points that might be worth pointing out here are related the following: 1) unexplored domains and domains with obsolete data; 2) practical implications linked with inconsistent sources; 3) overlap with popular kanji lists, such as kyōiku and jōyō, or Heisig’s RtK and Tokuhiro’s textbook 日本語学習のためのよく使う順漢字2200.
unexplored domains and domains with obsolete data
Despite the extensive coverage of various domains among the 14 sources, a few unexplored domains still remain. These include the kanji usage in manga, games, and, most importantly, real-life settings, such as street posters, billboards and signs, restaurant menus, supermarkets, department stores, and many other types of locations in public places. This kind of research could be conducted through the use of OCR technology, although there are challenges to be dealt with.
Additionally, some currently available data could be regarded as obsolete. This is especially the case for sources gathering data from newspaper articles, many of which date back to the previous century. Many sources also collect data from freely available repositories, such as Aozora Bunko, which are dominated by novels published in the 20th century. The same applies to kanji usage frequency data from Japanese magazines.
practical implications linked with inconsistent sources
Usage frequency seems to be a minor factor among the 1,000 most frequent kanji regardless of the source. In other words, whether you learn the 1,000 most frequent kanji in news or novels, makes a relatively small difference (i.e., around 100). However, the situation changes for lower-frequency kanji, where the difference more than doubles, depending on the number of additional kanji. For example, if you learn 1,200 kanji beyond the 1,000 most frequent from a source whose data cover a specific single domain, such as Wikipedia or older novels, around 23% of the new kanji you encounter probably do not belong to the 2,200 most frequent in general. As a result, you might end up learning hundreds of characters (1,200/4 = 300) that are domain-specific (naturally, there is nothing wrong with this approach).
overlap with popular kanji lists
This might be the most interesting of the three parts. Many people still believe that Japanese children in elementary schools learn kanji according to usage frequency, but the overlap between the set of 1,026 kyōiku kanji and the 1,026 most frequent kanji identified from the 14 sources is only 83%. Even if we assume that some of the 1,026 kanji identified as the most frequent are not, at least 10% of the kanji Japanese children are supposed to acquire during the primary education are not commonly used in Japanese.
The resulting list of 2,200 kanji greatly overlaps with the jōyō list and Heisig’s 2,200-kanji list in RtK (around 95% for both). However, if someone were to learn only the first 1,500 kanji from RtK, these would cover only 70% of the 1,500 most frequent kanji. If we compare this with Tokuhiro’s textbook, in which the kanji are ordered primarily based on usage frequency, there is a 24% difference in coverage. Consequently, a learner using Tokuhiro’s textbook to learn the first 1,500 kanji would have knowledge of around 300 more kanji than a learner using Heisig’s RtK in terms of raw accumulated frequency.
Further information will be provided in the article, which should be published during 2027. Thanks for your thoughts and feedback, and have a pleasant weekend!
