Map Labels in Multiple Scripts: How Google Maps and Waze Handle Unicode Differences

Why map labels in multiple scripts still break your in-car UX

Navigation apps promise a single source of truth for place names worldwide — but inconsistent Unicode handling, bidi mistakes, and poor font fallback make labels unreadable or misleading in-car. If you build or integrate mapping data (tile servers, fleet software, in-dash apps, or a navigation SDK), this article gives you the practical rules and code you need to get labels right across scripts, devices, and automotive displays in 2026.

Quick take — what matters most (first)

• Store native + transliteration: keep both the local-script name and a Latin transliteration; client decides which to show.
• Normalize to NFC: canonical composition avoids mismatches and visual splitting of grapheme clusters.
• Handle bidi with isolation: wrap mixed LTR/RTL labels using Unicode isolation controls or proper dir attributes.
• Bundle fallback fonts: for automotive displays include compact Noto subsets or use server-side rendered glyphs when shaping engines are limited.

How Google Maps and Waze approach native scripts vs. transliteration

Both Google Maps and Waze aim for readability, but their UX choices differ because of audience and product constraints. Understanding their approaches helps you design your labeling rules.

Google Maps (typical behavior)

• Prioritizes the local script for place labels (city names, POIs) when the map region is local to that script.
• Often shows a secondary transliteration or alternates between native and Latin based on zoom level, user language, or device locale.
• Uses CLDR and internal transliteration rules; Google invests in font fallback, shaping (HarfBuzz) and multi-layer rendering so mixed scripts look consistent on mobile and web.

Waze (typical behavior)

• Tends toward readability for drivers: transliteration to Latin may be favored on international devices or where the driving language differs from the native script.
• Community-edited names and shorter labels optimize for rapid glanceability — sometimes at the cost of native-script fidelity.
• Waze's label strategy is pragmatic: shorter, readable labels for fast scanning in-car; this can mean more transliterations or abbreviations.

Example (observed patterns): in Tokyo Google Maps displays both 渋谷 and "Shibuya" depending on zoom/locale, while Waze often shows "Shibuya" immediately on an international device for clarity. Use these patterns to set policy for your integration.

Storing names and normalization: the safe data model

Build your DB to support multi-script rendering and avoid surprises with simple rules:

1. Store a native_name (string) and a translit_name (Latin transliteration). Keep a third column for preferred display per locale.
2. Store an ISO language tag (BCP 47) per label — e.g., "ja", "ar-EG", "ru" — so clients know how to shape and order text.
3. Normalize input to NFC on ingest. Use Unicode Normalization Form C to avoid composed/decomposed mismatches in indexing and rendering.
4. Keep a script code (ISO 15924) for quick matching of fonts and fallback rules.

Why NFC?

Canonical composition (NFC) reduces false duplicates and visual glitches. Most rendering engines expect precomposed sequences for combining marks (e.g., Devanagari, Arabic diacritics). Use server-side normalization via ICU or platform APIs before saving.

// JavaScript (Node, modern engines)
const normalized = input.normalize('NFC');
// Python
import unicodedata
normalized = unicodedata.normalize('NFC', input)

Grapheme clusters and accessibility — don’t slice characters

One common bug: truncating a label in the middle of a grapheme cluster (emoji modifiers, composed letters, Indic conjuncts). Use grapheme-aware APIs.

// JavaScript: iterate by user-perceived characters
const seg = new Intl.Segmenter('en', {granularity: 'grapheme'});
const graphemes = [...seg.segment('काफ़ी')].map(s => s.segment);

For accessibility, announce the full native name to screen readers and provide the transliteration as an alternate text. Use aria-label/localization practices and avoid visual-only transliteration.

Bidi and mixed-direction labels: isolation is your friend

When a label mixes RTL and LTR scripts (e.g., Arabic + Latin or Hebrew + numbers), naive concatenation breaks reading order. Follow the Unicode Bidirectional Algorithm (UAX #9) and prefer embedding/isolation controls to avoid direction leakage in vehicle UIs.

Rules of thumb

• Wrap mixed-direction labels with RLI ... PDI (U+2067 .. U+2069) or LRI ... PDI to isolate the sequence.
• Use U+200E (LRM) or U+200F (RLM) when only a single directional mark is needed, but prefer isolation where available.
• Set dir attributes in web views and correct UI direction in native views.

// Example: show Latin transliteration inside an Arabic context
// Visible string: شارع الملك خالد (King Khalid St.)
// Safer to embed transliteration
const label = '\u202BRiyadh\u202C - \u202Bشارع الملك خالد\u202C';
// Better (isolation): use RLI/PDI
const safe = '\u2067Riyadh\u2069 - \u2067شارع الملك خالد\u2069';

Note: vehicle displays with limited bidi support (older infotainment stacks) may ignore isolation. For critical labels, provide a single-direction fallback (transliteration) in the UI options.

Font fallback and shaping for in-car displays

Potential problem: embedded systems lack comprehensive font stacks. Without proper glyphs or shaping engines, Arabic joins break and Indic scripts may render as separate consonants. Map label quality depends on two things: glyph coverage and OpenType shaping.

Shaping engines and embedded platforms

• HarfBuzz is the open-source shaping engine used in Android, many Linux stacks, and by Google. Ensure your in-dash environment has HarfBuzz or a capable shaping library.
• iOS uses Core Text/TextKit with its own shaping. CarPlay inherits iOS shaping behavior.
• If the platform lacks shaping, consider server-side glyph layout (rendering label images or signed distance fields) as a fallback.

Practical font fallback strategies

1. Include a compact set of Noto fonts covering your target markets. Noto Sans, Noto Sans Arabic, Noto Sans Devanagari, and Noto Emoji are good starting points.
2. Use a CSS or native font stack that ordering fonts by expected script, e.g.:

   font-family: "Noto Sans Arabic", "Noto Sans", "Segoe UI", sans-serif;

3. Detect missing glyphs at runtime and fall back to images for critical labels. A fast heuristic on web: draw text to canvas and compare measured width to width with known-unavailable-font to infer missing glyphs.
4. For automotive: ship subsetted Noto fonts in the vehicle build for supported regions to avoid runtime network dependencies.

Size and performance

Font files matter in vehicles with limited storage. Use subsetting tools (pyftsubset) to reduce Noto to specific Unicode ranges and OpenType tables required for shaping. For maps, prioritize coverages: Latin + local script + digits + common punctuation + currency symbols.

Rendering pipelines: what the client needs

Decide where rendering happens. There are three common approaches:

• Client-side text rendering (preferred): lets users select themes, use local fonts, and enables accessibility. Requires robust shaping and font fallback on-device.
• Server-side text rasterization: map tiles include pre-rendered labels (PNG/SVG). Guarantees visual consistency but loses selectable text and accessibility benefits.
• Hybrid: server provides glyph runs and shaping hints (using HarfBuzz on the server) to light clients that only need to blit glyphs.

In 2026, trends favor hybrid systems: cloud-assisted shaping that sends compact glyph runs to vehicles that lack full shaping engines, reducing bandwidth while preserving fidelity.

Transliteration: options and pitfalls

Transliteration is not one-size-fits-all. Use CLDR-provided transliteration rules where available and keep a human-review step for important POIs.

Options

• ICU Transliterator: programmatic transliteration (Java/C++/Android) using rules like Latin-ASCII or language-specific transliteration.
• Rule-based + ML hybrid: rule-based for predictable scripts (Cyrillic, Greek), ML for noisy OCR-derived labels or local romanization variants.
• Store canonical transliterations: let quality reviewers edit transliterations to match local signage and user expectation.

// Android (ICU) example
import android.icu.text.Transliterator;
Transliterator tr = Transliterator.getInstance("Any-Latin; Latin-ASCII");
String latin = tr.transliterate(nativeName);

Accessibility and testing checklist

Make testing part of CI/CD. Automate checks to prevent regressions across Unicode boundary cases.

1. Normalize and compare stored names using NFC; fail ingest if not normalized.
2. Run bidi sanity checks: ensure directionality matches language tag; run UAX #9 unit tests on mixed labels.
3. Segment labels into grapheme clusters and check truncation logic respects clusters.
4. Verify font coverage for each script targeted by your build; log missing glyphs.
5. Include screen-reader tests: announce native_name then translit_name as altText.
6. Smoke-test in-car targets: Android Automotive, CarPlay, QNX stacks used by integrators.

2026 trends and what's next

Recent industry momentum (late 2025 → early 2026) points to three important trends you should plan for:

• Cloud-assist shaping: hybrid glyph-run pipelines reduce client complexity while preserving fidelity for complex scripts in vehicles.
• Wider adoption of variable fonts: allows smaller font bundles that scale for legibility at different zoom/size levels — useful in automotive where DPI and viewing distance vary.
• Improved transliteration models: ML models fine-tuned on regional signage are increasingly common and are being integrated into mapping pipelines alongside CLDR rules.

Actionable checklist for mapping engineers (practical takeaways)

1. Store: native_name + translit_name + BCP47 language tag + script code.
2. Normalize all inputs to NFC using ICU or platform APIs on ingest.
3. Use Intl.Segmenter or ICU break iterators to avoid grapheme splits in truncation.
4. Wrap mixed-direction strings with LRI/RLI … PDI or set the correct dir context in UI elements.
5. Bundle compact Noto subsets per market; use server-side rasterization only when shaping engines are missing.
6. Implement a transliteration pipeline with ICU + human review for high-importance labels.
7. Automate tests for missing glyphs, bidi regressions, and grapheme truncation in CI.

Closing: build for clarity and safety in-vehicle

Map labels are tiny UI elements with outsized impact on safety and usability. As of 2026, hybrid rendering pipelines, compact font bundling, and strict Unicode hygiene (normalization, grapheme-aware truncation, and bidi isolation) are the best practices to make place names reliable across Google Maps-like clients, Waze-like quick-read interfaces, and constrained car displays.

Engineering for Unicode is not optional: it’s a cross-platform reliability problem that directly affects drivers' ability to read destinations quickly and safely.

If you want a quick starting kit: normalize to NFC, keep native + translit, isolate bidi runs, and bundle Noto subsets for target markets — then add automated tests for grapheme and glyph coverage. Those four moves fix 80% of real-world label issues.

Next step — try this

Run a simple experiment: export 100 high-traffic POI names from your database, ensure NFC normalization, and render them in three modes: client-side with bundled fonts, client-side without fonts (to detect missing glyphs), and server-side raster. Compare legibility and glyph fidelity on an actual in-car device. Document failures and iterate.

Want help? If you need a tailored assessment for your fleet or in-dash app, unicode.live offers audits and tooling to test normalization, font coverage, and bidi handling. Contact us to schedule a hands-on review.

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