Every Free Chinese Text Analyzer Answers the Wrong Question

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 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 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 reads pinyin off whole words instead of single characters.
• The character frequency counter 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 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.