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https://github.com/vrajvyas11/multilingual_purejs_based_ocr

Multilingual PureJS OCR: A zero-dependency JS library for accurate text extraction from images. Supports English, Chinese, Japanese, Korean & Latin via PaddleOCR ONNX models. Customizable detection, recognition & grouping. No Python needed—run in Node.js!
https://github.com/vrajvyas11/multilingual_purejs_based_ocr

image-processing multilingual no-python nodejs ocr onnxruntime-node optical-character-recognition pure-javascript text-detection-recognition

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Multilingual PureJS OCR: A zero-dependency JS library for accurate text extraction from images. Supports English, Chinese, Japanese, Korean & Latin via PaddleOCR ONNX models. Customizable detection, recognition & grouping. No Python needed—run in Node.js!

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README 

          
# Multilingual PureJS OCR 🚀



[![npm version](https://badge.fury.io/js/multilingual-purejs-ocr.svg)](https://www.npmjs.com/package/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



### Basic Usage - Different Languages



```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');

```



### Advanced Configuration



```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');

```



### Using Custom Models



```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'

});

```



### Performance Tuning with ONNX Options



```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'

  }

});

```



### Without Text Grouping



```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

```



### Dynamic Grouping Configuration



```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



### Complete Response Structure



```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



### Detection Options



| 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 |



### Recognition Options



| 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 |



### Grouping Options



| 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'

});

```



### Model Requirements



- **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



### Low Accuracy



- 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)



### Missing Text



- Lower `detectionThreshold`

- Reduce `minBoxSize`

- Increase `unclipRatio`



### Performance Issues



- Reduce `maxImageSize`

- Adjust ONNX thread settings

- Process images in batches



### Wrong Grouping



- Adjust grouping ratios in configuration

- Use `grouped: false` and implement custom grouping



## 📝 API Reference



### `Ocr.create(options)`



Creates a new OCR instance with the specified configuration.



**Parameters:**

- `options` (Object): Configuration options



**Returns:** `Promise` - OCR instance



### `ocr.detect(imagePath, options)`



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` - Detection results



### `ocr.setGroupingConfig(config)`



Updates the text grouping configuration.



**Parameters:**

- `config` (Object): New grouping configuration



### `ocr.getGroupingConfig()`



Returns the current grouping configuration.



**Returns:** `Object` - Current grouping configuration



### `Ocr.getAvailableLanguages()`



Static method that returns available languages.



**Returns:** `Object` - Language configurations



## 🤝 Contributing



Contributions are welcome! Please feel free to submit issues or pull requests.



## 📄 License



ISC



## 🙏 Acknowledgments



- Built on [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) models

- Uses [ONNX Runtime Node](https://www.npmjs.com/package/onnxruntime-node) for efficient model inference in Node.js

- Powered by [Sharp](https://sharp.pixelplumbing.com/) for image processing



## 📧 Support



For issues and questions, please open an issue on [GitHub](https://github.com/VrajVyas11/Multilingual_PureJS_Based_OCR/issues).



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