# Every Free Chinese Text Analyzer Answers the Wrong Question

URL: https://fulinlabs.com/blog/free-chinese-text-analyzer-known-words-2026/
Published: 2026-09-04
Product: hsk-studio
Summary: Free Chinese text analysers tell you how hard a text is in general. The useful question is how hard it is for you — which needs a model of what you already know. Here's why the tools that do that are paid desktop apps, and what I built instead.

I spent an afternoon looking at what the free Chinese text tools actually do, because I was about to
build some and did not want to build the tenth copy of something that already exists nine times.

The short version: they nearly all answer a question about the text, and the question worth asking is
about you.

## What's already out there

Search for a Chinese pinyin converter and you get a crowded, mature field — Purple Culture, Chinese
Converter, DigMandarin, Laoshi, StudyCLI, JCinfo, and Google Translate sitting above all of them.
Purple Culture in particular is very good: tone colours, per-syllable audio, image OCR, file upload,
hover definitions, and an opt-in fix for 一 and 不 tone sandhi. Search for an HSK text analyser and
you get a similar pile, several of them clearly built for the search term rather than for a reader.

These tools are commodities in the precise sense: they are interchangeable, they are free, and the
best one is already free. Building another is not a product decision, it is a coin flip on domain
authority.

But every one of them, the good ones included, is *stateless*. You paste, it computes, it forgets
you. That is fine for a one-off conversion. It is close to useless for studying, because it means the
tool can only ever tell you how hard a text is in the abstract.

## The question that actually matters

"What HSK level is this article?" is answerable from the article alone. It is also not the question
anyone has. The question people have is "can I read this?" — and that depends on the few thousand
words you happen to have met, not on a syllabus.

Two texts at the same HSK level are not equally readable to you. A text one level above you that
happens to reuse vocabulary you already know is easier than a text at your level built out of the
two hundred words you never learned. The level is a property of the text; readability is a
relationship between the text and your head.

To report on that relationship, a tool has to remember what you know.

## How the paid tools solve it, and what it costs

There is a well-known program that does exactly this: Chinese Text Analyser, a paid desktop app. Its
feature list leads with "easily see which words you do and do not know in any piece of Chinese text",
and people on r/ChineseLanguage recommend it specifically for reading novels.

The workflow they describe is the interesting part:

1. Export your known words from Anki or Pleco to a text file.
2. Import that file into the analyser to populate its known-word list.
3. Analyse the novel.
4. Export the unknown words, sorted by frequency.
5. Import that file back into Anki or Pleco.

It works. People genuinely do it. But look at why it takes five steps: the analyser and the flashcard
program are different pieces of software that have to be introduced to each other, over and over,
via the filesystem. Every one of those steps is friction that exists for architectural reasons rather
than pedagogical ones.

## What I built

HSK Studio already had a review deck — words you save while reading get scheduled with spaced
repetition, stored in your browser, no account. So the known-word list was already there. It just was
not being used for anything except review.

The [Chinese text analyzer](https://hsk.fulinlabs.com/tools/chinese/text-analyzer) uses it. Paste a
text and you get every word in it ranked by frequency, with the ones already in your deck marked off,
so the list you are looking at is the list of words you personally still need. Select the ones worth
learning and they go into the deck — no export, no import, because the analyser and the deck are the
same program. If you keep your cards elsewhere, it still exports to Anki, Pleco or CSV.

It also reports comprehension coverage against your own vocabulary, measured in running words rather
than distinct words. That distinction matters more than it sounds: if 的 appears forty times, that is
forty easy encounters. Counting it once makes ordinary prose look far harder than it reads.

Three companion tools sit on the same engine, because the same segmentation pass answers different
questions:

- The [HSK level checker](https://hsk.fulinlabs.com/tools/chinese/hsk-level-checker) gives the
  conventional verdict — the lowest HSK 3.0 level covering 90% of the running words — plus the
  cumulative coverage curve behind it, so you can see what finishing the next level would buy you on
  *this* passage.
- The [pinyin converter](https://hsk.fulinlabs.com/tools/chinese/pinyin-converter) reads pinyin off
  whole words instead of single characters.
- The [character frequency counter](https://hsk.fulinlabs.com/tools/chinese/character-frequency)
  ranks characters with a running total, which is the honest argument for learning frequent
  characters first.

## The one place a converter can still be better

I said pinyin conversion is a commodity, and mostly it is. There is one exception worth explaining,
because it is the difference between a converter that is right and one that is usually right.

Around a thousand common characters have more than one reading, and which one applies depends on the
word. 行 is *xíng* in 行李 and *háng* in 银行. 乐 is *lè* in 快乐 and *yuè* in 音乐. 长 is *cháng* when
it means long and *zhǎng* when it means to grow. A converter that walks through your text one
character at a time has to guess, and it guesses the most common reading.

Segmenting into words first removes the guess, because the reading comes from the word. That is also
what lets each syllable sit above the right character: the dictionary reading for 银行 is split into
yín and háng and each is pinned to its own hanzi. Every one of the 11,643 entries in the dictionary
is checked to produce exactly one syllable per character, so the alignment is guaranteed rather than
hoped for.

Then there is sandhi. Inside a word the dictionary already handles it — 一起 is stored as *yìqǐ*, not
*yīqǐ*. Between words nothing can, because the pair only exists in your text. 不是 is not a
dictionary entry, so a per-word lookup gives *bù shì* where every speaker says *bú shì*. So 一 and 不
are adjusted across word boundaries, and everything that changed is listed under the result instead
of being silently corrected.

Third-tone sandhi — 你好 said as *níhǎo* — is a toggle rather than a default. It is real in speech,
conventionally not written, and in longer chains the grouping depends on phrasing in a way no
automatic rule settles. Anything that honest has to be optional.

## What it does not do

The dictionary is 11,643 entries covering HSK 1-9. Words outside it fall back to character-by-character
readings, which is exactly where the guessing returns — names especially. 行长 is not an HSK word, so
it comes back as *xíng cháng* rather than *hángzhǎng*. Segmentation is longest-match, which is not
perfect on rare compounds; where it fails it splits into shorter real words rather than inventing
anything.

And vocabulary coverage is only the part of difficulty that can be counted. Sentence length, grammar,
register, how much the writer assumes — none of that is in any of these numbers, and all of it
matters. A children's story and a legal notice can share an HSK level and share nothing else. The character-coverage half of the picture, with a method you can reproduce, is worked through in [How Hard Is a Chinese Text? Measuring Difficulty by HSK Character Coverage](/blog/how-hard-is-a-chinese-text-coverage-method-2026/).

## The general lesson

The advice I was given was to build the highest-volume, easiest tools first: pinyin converter,
vocabulary extractor, character counter. That is reasonable-sounding advice and it would have been a
waste, because "highest volume and easiest to build" describes precisely the tools that already exist
in bulk and are already good.

The gap was not a missing tool. It was a missing *connection* — between the thing that analyses text
and the thing that remembers what you know. Those were separate programs for everyone else, and they
did not have to be for me.

Everything runs in the browser. Nothing you paste is uploaded, and there is no account.

## FAQ

### What is a Chinese text analyzer?

A tool that splits Chinese text into words — Chinese is written without spaces, so this has to be done before anything else — and then reports on those words: how many there are, how often each appears, and which HSK level each belongs to. The better ones also tell you which of the words you personally do not know yet, which requires the tool to store a list of your known words.

### Is there a free alternative to Chinese Text Analyser?

Chinese Text Analyser is a paid desktop program, and its central feature — marking which words you do and do not know — is what free web tools generally lack. The HSK Studio text analyzer is a free browser version of that idea: it treats the words you have saved in your review deck as your known-word list, so it can show you only the words in a text you have not met, and export them to Anki, Pleco or CSV.

### Why do most free Chinese analysers only tell me the HSK level?

Because HSK level is a property of the text alone, so it can be computed from a paste with no memory of who you are. Telling you which words you do not know requires storing your vocabulary between visits, which means either an account or browser storage. Most free tools are single-page utilities with neither.

### How much of a text do I need to know to read it comfortably?

Research on extensive reading puts comfortable independent reading at around 98% of running words known, and workable-with-a-dictionary at around 95%. Below about 90% a text is better used as a source of new vocabulary than as reading practice. Note that these are running words — every occurrence counts — not distinct words.

### Does a character-level analysis work just as well as a word-level one?

No, and it consistently makes texts look easier than they are. Characters are shared between easy and hard words: knowing 银 tells you nothing about 银河系, and knowing 电 and 脑 does not mean you know 电脑. Character coverage overstates readability, which is why level estimates are better computed over words.