Thai Address Splitter

split long address string(in Thai) to 'name', 'phone number', 'subdistrict', 'district', 'province', 'zipcode'

Architecture

This library uses a class-based architecture with separated concerns:

- ThaiAddressSplitter - Main orchestrator class
- TextPreprocessor - Text cleaning and normalization
- LocationMatcher - Subdistrict database matching and scoring
- EntityExtractor - Name, phone, and address extraction
- Constants - Centralized patterns, keywords, and mappings

How It Works

``mermaid
flowchart LR
A[Thai Address String] --> B[Clean Text]
B --> C[Extract Thai Words]
C --> D[Find Location Match]
D --> E[Extract Entities]
E --> F[Structured Output]

subgraph "Core Steps"
B
C
D
E
end

subgraph "Data Sources"
DB[(subdistricts.json)]
PATTERNS[Regex Patterns]
end

D -.-> DB
E -.-> PATTERNS
`

$3

1. Text Preprocessing:
- Removes administrative prefixes (เขต, แขวง, จังหวัด, อำเภอ, ตำบล, etc.)
- Normalizes whitespace and cleans input
2. Word Extraction: Splits text and filters Thai words with at least 2 characters
3. Location Matching:
- Searches each word against the subdistricts database
- Calculates frequency scores for location confidence
4. Entity Extraction:
- Removes location words from text (zipcode → province → district → subdistrict)
- Extracts names with title prefixes (นาย, นาง, คุณ, etc.) or without titles
- Extracts phone numbers (starting with 06, 08, 09)
- Remaining text becomes the address field

Example

basic split
`js
const Splitter = require('../splitter');

(async () => {
const input = 'คุณดอกฝ้าย สายเขียว 799/11 ถนนจักรแก้ว แขวงวังบูรพาภิรมย์ เขตพระนคร กรุงเทพ 10200 เบอร์ 0911222333';
const result = Splitter.split(input);
console.log('result :', { input, result });
})();
`

Tests


`
pnpm run test
``