Normalize Before You Personalize: Multilingual Handwriting Data at Scale

Advanced Problem

Multilingual handwriting systems degrade quickly when normalization is inconsistent across data sources. You get duplicated symbols, conflicting stroke orders, and broken downstream analytics.

Step 1: Define canonical entry schema

{
  "char": "愛",
  "unicode": "U+611B",
  "language": "ja",
  "strokes": ["..."]
}

Step 2: Build deterministic normalization transforms

def normalize_entry(entry):
    return {
        "char": entry["char"].strip(),
        "unicode": entry["unicode"].upper(),
        "language": entry["language"].lower(),
        "strokes": list(entry.get("strokes", [])),
    }