Multilingual PureJS OCR 🚀


A powerful onnxruntime-node based, pure JavaScript OCR library that works directly in Node.js - no Python dependencies required! Extract text from images with industry-leading accuracy using PaddleOCR models.

✨ Features

- 🌍 Multilingual Support: English, Chinese, Japanese, Korean, and Latin languages
- 🎯 High Accuracy: Based on PaddleOCR v3/v4 models
- 📦 Zero Python Dependencies: 100% JavaScript/Node.js implementation
- 🧠 ONNX Runtime Node: Efficient, high-performance model inference directly in Node.js
- 🔧 Highly Customizable: Configure detection, recognition, and grouping parameters
- 🎨 Smart Text Grouping: Automatically groups text into paragraphs
- 🔌 Bring Your Own Models: Use custom ONNX models
- ⚡ Production Ready: Built with performance and reliability in mind
- 🚀 Plug and Play: All models and dictionaries included - just install and run!

📥 Installation

``bash
npm install multilingual-purejs-ocr
`

🚀 Quick Start

`javascript
import Ocr from 'multilingual-purejs-ocr';

// Create OCR instance with default settings (English)
const ocr = await Ocr.create();

// Detect text in an image
const result = await ocr.detect('./image.jpg');

console.log('Total elements:', result.totalElements);
console.log('Paragraphs:', result.paragraphs);
console.log('Individual elements:', result.data);
`

📖 Usage Examples

$3

`javascript
// English (default)
const ocrEn = await Ocr.create({ language: 'en' });
const resultEn = await ocrEn.detect('./english.jpg');

// Chinese
const ocrCh = await Ocr.create({ language: 'ch' });
const resultCh = await ocrCh.detect('./chinese.jpg');

// Japanese
const ocrJa = await Ocr.create({ language: 'ja' });
const resultJa = await ocrJa.detect('./japanese.jpg');

// Korean
const ocrKo = await Ocr.create({ language: 'ko' });
const resultKo = await ocrKo.detect('./korean.jpg');

// Latin languages (Portuguese, Spanish, French, etc.)
const ocrLatin = await Ocr.create({ language: 'latin' });
const resultLatin = await ocrLatin.detect('./latin.jpg');
`

$3

`javascript
const ocr = await Ocr.create({
// Language selection
language: 'en',

// Detection parameters
detectionThreshold: 0.1, // Lower = more sensitive (0-1)
minBoxSize: 3, // Minimum text box size in pixels
maxBoxSize: 2000, // Maximum text box size in pixels
unclipRatio: 1.5, // Box expansion ratio

// Recognition parameters
confidenceThreshold: 0.5, // Minimum confidence to accept text (0-1)
imageHeight: 48, // Recognition model input height
removeDuplicateChars: true, // Remove duplicate characters

// Text grouping configuration
grouping: {
verticalThresholdRatio: 1.2,
horizontalThresholdRatio: 2.5,
minOverlapRatio: 0.3,
maxVerticalOffsetRatio: 0.5
}
});

const result = await ocr.detect('./image.jpg');
`

$3

`javascript
const ocr = await Ocr.create({
language: 'en',
detectionModelPath: './my-models/custom_detection.onnx',
recognitionModelPath: './my-models/custom_recognition.onnx',
dictionaryPath: './my-models/custom_dict.txt'
});
`

$3

`javascript
const ocr = await Ocr.create({
language: 'en',

// Detection model ONNX options
detectionOnnxOptions: {
executionProviders: ['cpu'],
intraOpNumThreads: 4, // Use 4 CPU threads
graphOptimizationLevel: 'all',
enableCpuMemArena: true
},

// Recognition model ONNX options
recognitionOnnxOptions: {
executionProviders: ['cpu'],
intraOpNumThreads: 2,
executionMode: 'sequential'
}
});
`

$3

`javascript
const ocr = await Ocr.create({ language: 'en' });

// Get only individual text elements (no paragraph grouping)
const result = await ocr.detect('./image.jpg', { grouped: false });

console.log(result.data); // Array of individual text elements
// result.paragraphs will not be present
`

$3

`javascript
const ocr = await Ocr.create({ language: 'en' });

// Update grouping configuration at runtime
ocr.setGroupingConfig({
verticalThresholdRatio: 1.5,
horizontalThresholdRatio: 3.0
});

// Get current configuration
const config = ocr.getGroupingConfig();
console.log(config);
`

📊 Output Format

$3

`javascript
{
totalElements: 31, // Total text elements detected
totalParagraphs: 9, // Number of grouped paragraphs

// Grouped paragraphs (when grouped: true)
paragraphs: [
{
text: "BECAUSE OF YOU,",
confidence: 0.923,
boundingBox: {
left: 485,
top: 61,
width: 84,
height: 43
},
elements: [ // Individual elements in this paragraph
{
text: "BECAUSE",
confidence: 0.96,
frame: { left: 485, top: 61, width: 84, height: 20 }
},
{
text: "OF YOU,",
confidence: 0.86,
frame: { left: 489, top: 81, width: 75, height: 23 }
}
]
}
],

// Individual text elements
data: [
{
text: "BECAUSE",
confidence: 0.9598,
frame: {
left: 485,
top: 61,
width: 84,
height: 20
},
box: [[485, 61], [569, 61], [569, 81], [485, 81]] // 4-point polygon
}
]
}
`

🎛️ Configuration Options

$3

| Option | Type | Default | Description |
|--------|------|---------|-------------|
| detectionThreshold | number | 0.1 | Text detection sensitivity (0-1, lower = more sensitive) |
| minBoxSize | number | 3 | Minimum text box size in pixels |
| maxBoxSize | number | 2000 | Maximum text box size in pixels |
| unclipRatio | number | 1.5 | Box expansion ratio for better text capture |
| baseSize | number | 32 | Image size base unit (must be multiple of 32) |
| maxImageSize | number | 960 | Maximum image dimension |

$3

| Option | Type | Default | Description |
|--------|------|---------|-------------|
| language | string | 'en' | Language code: en, ch, ja, ko, latin |
| confidenceThreshold | number | 0.5 | Minimum confidence to accept text (0-1) |
| imageHeight | number | 48 | Recognition model input height |
| removeDuplicateChars | boolean | true | Remove consecutive duplicate characters |

$3

| Option | Type | Default | Description |
|--------|------|---------|-------------|
| verticalThresholdRatio | number | 1.2 | Max vertical gap ratio for grouping |
| horizontalThresholdRatio | number | 2.5 | Max horizontal gap ratio for same line |
| minOverlapRatio | number | 0.3 | Minimum vertical overlap for same line |
| maxVerticalOffsetRatio | number | 0.5 | Max vertical offset ratio for same line |

🌍 Supported Languages

`javascript
// Get list of available languages
const languages = Ocr.getAvailableLanguages();
console.log(languages);
`

| Code | Language | Model |
|------|----------|-------|
| en | English | PP-OCRv4 |
| ch | Chinese | PP-OCRv4 |
| ja | Japanese | PP-OCRv3 |
| ko | Korean | PP-OCRv4 (Korean dict) |
| latin | Latin (PT, ES, FR, etc.) | PP-OCRv3 |

📁 Included Models

The package ships with all required models and dictionaries for plug-and-play usage:

`
models/
├── ch_PP-OCRv4_det_infer.onnx # Detection model (shared)
├── en_PP-OCRv4_rec_infer.onnx # English recognition
├── en_dict.txt # English dictionary
├── ch_PP-OCRv4_rec_infer.onnx # Chinese recognition
├── ch_dict.txt # Chinese dictionary
├── japan_PP-OCRv3_rec_infer.onnx # Japanese recognition
├── japan_dict.txt # Japanese dictionary
├── korean_dict.txt # Korean dictionary
├── latin_PP-OCRv3_rec_infer.onnx # Latin recognition
└── latin_dict.txt # Latin dictionary
`

No additional setup required - everything is ready to use out of the box!

🔧 Custom Models

You can use your own ONNX models:

`javascript
const ocr = await Ocr.create({
detectionModelPath: './path/to/detection.onnx',
recognitionModelPath: './path/to/recognition.onnx',
dictionaryPath: './path/to/dictionary.txt'
});
`

$3

- Detection Model: ONNX format, input shape [1, 3, H, W], output: binary mask
- Recognition Model: ONNX format, input shape [1, 3, 48, W], output: character probabilities
- Dictionary: Text file with one character per line

🎯 Use Cases

- 📸 Document Scanning: Extract text from scanned documents
- 🎫 Receipt Processing: Parse receipts and invoices
- 🚗 License Plate Recognition: Read vehicle plates
- 📝 Form Processing: Extract data from forms
- 🌐 Translation Apps: Detect and translate text in images
- 📱 Screenshot OCR: Extract text from screenshots
- 🏷️ Label Reading: Read product labels and barcodes

⚡ Performance Tips

1. Adjust Detection Threshold: Lower for harder-to-read text, higher for cleaner images
2. Use Appropriate Language Model: Select the correct language for better accuracy
3. Optimize Image Size: Resize very large images before processing
4. Tune ONNX Threads: Adjust intraOpNumThreads based on your CPU cores
5. Disable Grouping: Use grouped: false if you only need individual elements

🐛 Troubleshooting

$3

- Adjust detectionThreshold (try 0.05 for harder images)
- Increase unclipRatio (try 1.8 or 2.0)
- Ensure correct language model is selected
- Preprocess image (increase contrast, remove noise)

$3

- Lower detectionThreshold
- Reduce minBoxSize
- Increase unclipRatio

$3

- Reduce maxImageSize
- Adjust ONNX thread settings
- Process images in batches

$3

- Adjust grouping ratios in configuration
- Use grouped: false and implement custom grouping

📝 API Reference

$3

Creates a new OCR instance with the specified configuration.

Parameters:
- options (Object): Configuration options

Returns: Promise - OCR instance

$3

Detects and recognizes text in an image.

Parameters:
- imagePath (string): Path to image file
- options (Object): Detection options
- grouped (boolean): Return grouped paragraphs (default: true)
- onnxOptions (Object): Runtime ONNX options

Returns: Promise