qurancoor 1.1.1

Pixel-accurate word coordinates for every word in the Quran (Madani mushaf)

Quran Word Coordinates

Pixel-accurate word coordinates for every word in the Quran (Madani mushaf). Maps each of the 77,320 words across 604 pages to its exact bounding box on the page image.

Install

Python (full system)

pip install qurancoor              # Data API only
pip install qurancoor[serve]       # + web viewer/editor
pip install qurancoor[generate]    # + coordinate generation tools
pip install qurancoor[all]         # everything

JavaScript / TypeScript

npm install quran-word-coords

Quick Start

Python API

from qurancoor import get_page, get_word, find_word_at, get_ayah, scale_coords

# Get all word coordinates on page 7
coords = get_page(7)
# => {"2:38:1": {"h": {"x": 818, "y": 23, "w": 61, "h": 68}, ...}, ...}

# Get a specific word: Sura 2, Ayah 255 (Ayat Al-Kursi), Word 1
word = get_word(2, 255, 1)
print(word["h"])  # {"x": ..., "y": ..., "w": ..., "h": ...}

# Find which word is at pixel (820, 40) on page 7
location = find_word_at(7, 820, 40)
# => "2:38:1"

# Get all words in Al-Fatiha, Ayah 1
words = get_ayah(1, 1)

# Scale to a different display size
scaled = scale_coords(word["h"], target_width=1800, target_height=2874)

CLI Commands

# Launch the web viewer (requires mushaf page images)
qurancoor serve --images-dir ./images --port 8003

# Generate coordinates from source images
qurancoor generate --build-db -q quran.com-images
qurancoor generate -b . -q quran.com-images -o output --all

# Build word frequency database
qurancoor build-freq --mushaf-dir ./mushaf --db word_freq.db

JavaScript / TypeScript

import { getPage, getWord, findWordAt, getAyah, scaleBox } from 'quran-word-coords';

const coords = getPage(7);
const word = getWord(2, 255, 1);
const location = findWordAt(7, 820, 40); // => "2:38:1"
const ayah = getAyah(1, 1);
const scaled = scaleBox(word!.h, 1800, 2874);

API Reference

Function	Python	npm	Description
Get page coords	get_page(7)	getPage(7)	All word boxes on a page (1-604)
Get word coords	get_word(2, 255, 1)	getWord(2, 255, 1)	Specific word by sura:ayah:word
Hit-test	find_word_at(7, 820, 40)	findWordAt(7, 820, 40)	Which word is at (x,y)?
Get ayah words	get_ayah(2, 255)	getAyah(2, 255)	All words in an ayah
Scale coords	scale_coords(box, w, h)	scaleBox(box, w, h)	Adapt to different image sizes
Word count	word_count(7)	wordCount(7)	Number of words on a page
All locations	all_locations(7)	allLocations(7)	Sorted word keys on a page
Free memory	clear_cache()	clearCache()	Release cached data

Coordinate Format

Each word has up to 3 coordinate boxes, all in pixels relative to the page image (900 x 1437):

{
  "2:38:1": {
    "h": { "x": 818, "y": 23, "w": 61, "h": 68 },
    "p": { "x": 818, "y": 0,  "w": 61, "h": 23 },
    "o": { "x": 0,   "y": 23, "w": 39, "h": 68 }
  }
}

Box	Name	Purpose
h	Highlight	Tight bounding box around the word ink
p	Padding	Space above the text line (for tooltips/popovers)
o	Origin	Left margin area (for line markers)

Key format: "sura:ayah:word" — e.g., "2:38:1" = Sura 2 (Al-Baqarah), Ayah 38, Word 1.

Practical Use Cases

1. Word-by-word highlighting during audio recitation — map audio timestamps to word coordinates
2. Tap-to-show tafsir — hit-test user taps against word boxes on mushaf images
3. Visual search results — highlight found words directly on mushaf pages
4. Tajweed overlay — color-code words with tajweed rules at exact positions
5. Qira'at (variant readings) — mark words with reading differences
6. Word frequency tools — tap a word to see all its occurrences across the Quran

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How Word Positioning Works

The system achieves pixel-accurate coordinates through a 5-step pipeline:

Step 1: Font Metric Analysis

Each page uses a dedicated QCF font (QCF_P001.TTF ... QCF_P604.TTF) where every word is a single glyph. The system extracts the advance width of each glyph from the font's hmtx table to determine proportional spacing.

Step 2: Image Line Detection

Horizontal projection analysis on the grayscale page image identifies text line bands — consecutive rows with ink above a threshold. Standard pages have 15 lines; sura opening pages have 8.

Step 3: Glyph Database Lookup

From quran_glyphs.db (built from quran.com-images), the system retrieves ALL glyphs on each line — words, ayah markers, and waqf marks. Non-word elements occupy physical space and must be accounted for.

Step 4: Proportional Cutting with Gap Snapping

The core algorithm:

1. Proportional cuts from font advance widths give expected boundary positions
2. Gap detection scans actual image pixels for vertical whitespace columns
3. Snap to gaps aligns each cut to the nearest real gap using a scoring function:

   score = gap_width * 2.0 - distance_from_expected * 0.5
   

4. Minimum width enforcement prevents collapsed boxes (< 25% expected width)

Step 5: Mushaf JSON Alignment

The quran.com-images database numbers waqf marks as word positions, but the mushaf JSON skips them — causing position misalignment. The system matches word boxes to mushaf words by sequential order within each line, not by position numbers, ensuring every word maps correctly.

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Repository Structure

qurancoor/
  pyproject.toml              # Python package config
  src/qurancoor/
    __init__.py               # Data API (get_page, get_word, etc.)
    cli.py                    # CLI entry point (qurancoor command)
    server.py                 # Web viewer/editor (FastAPI)
    generate.py               # Coordinate extraction pipeline
    build_freq.py             # Word frequency database builder
    data/                     # 604 coordinate JSON files (bundled)
  npm/                        # npm package
    package.json
    src/index.ts
    data/coords.json
  data/
    coords/                   # Alternative coordinate data
    pages/farsh/              # Qira'at variant readings
  quran.com-images/           # Submodule: fonts, SQL data

Building from Source

Only needed if regenerating coordinates from mushaf images.

pip install qurancoor[all]

# Build glyph database
qurancoor generate --build-db -q quran.com-images

# Generate all page coordinates
qurancoor generate -b . -q quran.com-images -o output --all

# Build word frequency database
qurancoor build-freq --mushaf-dir ./mushaf --db word_freq.db

# Launch viewer
qurancoor serve --images-dir ./images --port 8003

License

MIT. The mushaf images and quran.com-images data have their own licenses — see quran.com-images.