If you want to break traditional Hanzi into their components programmatically, the data exists, and it is genuinely useful. Character-decomposition datasets can split a character into a tree of parts that software can traverse. For understanding structure and building tools, this is great. For learning to write, it is, like all understanding aids, only half the story.

What decomposition data offers

There are open resources for this:

  • Ideographic Description Sequences (IDS), a standard way to describe a character as components combined by position operators (left-right, top-bottom, enclosure).
  • Projects like CHISE and various open decomposition databases that provide component trees for large character sets, including traditional forms.

With these, you can algorithmically decompose 體 or 鬱 into their parts, build flashcards by shared components, or analyse which components recur. For a PKM-minded or programming learner, that is a powerful raw material, related to the breakdown discussion in etymology breakdown plus writing.

Two important caveats

Before relying on it:

  • Decompositions vary by source. Different datasets split characters differently, and there is no single canonical decomposition. Treat any one as a view, not the truth.
  • Visual versus functional. Some decompositions describe how a character looks split up (visual structure); others aim at etymological components that carry meaning and sound. These can differ, and the etymological kind, like Outlier Linguistics, is what actually explains a character, whereas a purely visual split can mislead. Know which kind you have.

So algorithmic breakdown is useful but not infallible, and its meaning depends on the dataset’s intent.

The limit: understanding is not writing

Even a perfect decomposition is understanding, not recall. Knowing that a character is component A over component B helps you remember it, but it does not make your hand able to produce it from memory, the recognition-versus-recall gap from the case for a dedicated Hanzi writing app. Decomposition gives you the map; writing is the territory.

So use breakdown to understand structure and to organise study, then convert that understanding into writing through from-memory practice.

Where Hanzi Write Practice fits

Hanzi Write Practice is not a decomposition engine, and it would be wrong to pitch it as one, bring your own component data from IDS, CHISE, or a dictionary if you like analysis. What it does is the writing step: you produce each character from memory on a grid, then check stroke order, pinyin, and meaning, with spaced repetition. So the structure you decomposed becomes a character you can write.

Decompose to understand, ideally with functional, not just visual, data. Then write from memory to learn. The algorithm maps the character; your hand has to make it.

Join early access and turn component trees into characters you can write.