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Early Concept
Pingilish — Standardizing Persian in Latin Script
Pingilish is an early-stage concept exploring a standardized way to
write Persian using the Latin alphabet. The project investigates how a
clear and consistent transliteration system could improve readability,
learning, and technical use cases.
Problem
Persian speakers widely use Latin script (often called
“Pingilish”) in digital contexts, but:
- There is no shared convention.
-
Words are written inconsistently (e.g. doost, dost, dust (meaning
friend)).
- Pronunciation details are often lost.
-
This creates friction for learners, developers, and systems.
Opportunity
There is space for a reference system that:
- Reduces ambiguity.
- Improves learning and pronunciation.
- Enables consistent processing in digital products.
Concept Direction
Pingilish explores a transliteration approach based on:
- One sound → one representation.
- ASCII-only characters (no special symbols).
- Explicit vowel length (aa / i / oo).
- Consistent consonant mapping (sh, ch, kh, zh).
Design Hypothesis
If Persian sounds are represented consistently and predictably in
Latin script, then:
- Learners will better understand pronunciation.
- Developers can process text more reliably.
- A shared reference may emerge over time.
Early Design Principles
-
Clarity over familiarity
— avoid ambiguous common spellings.
-
Consistency over flexibility
— minimize multiple valid forms.
-
Accessibility — work on
any standard keyboard.
-
Scalability — usable in
tools, datasets, and products.
Target Audience (Exploration)
Persian language learners
Developers & researchers
Governments (Public Signs/Digital Names like domains)
Next Steps
-
Validate the system with real users (learners & developers).
- Prototype a transliteration tool.
- Test readability vs familiarity trade-offs.
- Explore positioning as a reference standard.
Example Transliterations
Illustrative mappings (conceptual — not a full specification):
فارسی
→
faarsi
خوب
→
khoob
سلام
→
salaam
Reflection
This concept explores how system design thinking can be applied to
language representation. The focus is not on replacing existing
behaviors, but on introducing a structured alternative where
consistency matters.