Public Text Tone Mark Count Tool

Description

Optionally NFD-decompose text, then count code points exactly matching those entries.

Text may encode a visible mark as a combining code point or within a precomposed character. Unicode decomposition and a source-specific ordering list address different representation questions. Counting supplied tone-mark symbols does not infer a language’s tones, pronunciation or appropriate orthography.

Optionally NFD-decompose text, then count code points exactly matching those entries.

When to use this tool

Use Public Text Tone Mark Count when checking the stated supplied mark-counting or ordering convention.

How to use the tool and how it works

Enter text, suppliedToneMarks, decompose. Use the units and conventions below.
Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
Read count. Use the output convention below.

Require unique supplied tone-mark entries each containing one Unicode code point. Optionally NFD-decompose text, then count code points exactly matching those entries.^[1]

Code points, UTF-16 code units, canonical decomposition and case conversion are different operations. This helper follows its explicit local convention rather than linguistic analysis or rendered-text measurement.^[2]

Input field	Meaning and units	Accepted input
text	Text	Required. Text; maximum UTF-16 length 8192.
suppliedToneMarks	Supplied Tone Marks	Required. Array of text; maximum UTF-16 length 8192; maximum items 256.
decompose	True applies NFD only to text, leaving suppliedToneMarks unchanged.	Required. Boolean.

Output field	Interpretation	Returned shape
count	Count	Required. Safe integer.

json

{
  "input": {
    "text": "áà",
    "suppliedToneMarks": [
      "́",
      "̀"
    ],
    "decompose": true
  },
  "output": {
    "count": 2
  }
}

Limitations and assumptions

Only text is normalized, not the supplied mark list; this counts symbols rather than inferring tones, pronunciation or a language-specific orthography.
Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.

Alternative or Complementary methods

Use the source’s orthographic policy and appropriate Unicode normalization or language-aware analysis when linguistic interpretation or text equivalence matters.

References

Calculation and input contract: african-studies/public-text-tone-mark-count — tool.io
Unicode 17.0: encoding and normalization conventions — Unicode Consortium

Similar or alternative tools

Public Source Tone Mark Order
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Disclosure Character Budget Gap
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