@polina123/node-red-ocr-image-to-text 1.0.5

Node-RED node for text recognition from images and PDF files using EasyOCR/PaddleOCR/Tesseract. Supports 20+ languages, parallel processing (up to 10x speedup), PDF text extraction, and automatic text orientation detection.

npm install @polina123/node-red-ocr-image-to-text

Node-RED Image OCR (Multi-Engine)

npm version

Node-RED node for automatic text recognition from images and PDF files using multiple OCR engines: PaddleOCR (default), EasyOCR, and Tesseract. Supports 20+ languages with parallel processing for up to 10x speedup.

Features

Three OCR Engines: PaddleOCR (recommended), EasyOCR, Tesseract
Text Recognition from JPG, JPEG, PNG images and PDF files
PDF Support - extract text layer or OCR scanned PDFs
Auto-Rotate - automatic text orientation detection and correction
Multi-language - support for 20+ languages simultaneously
Parallel Processing - up to 8-10x speedup for multiple images
Speed Optimization - image resizing before processing
Flexible Configuration - choose OCR engine, languages, GPU, quality control
Error Handling - detailed processing status information
Performance Monitoring - processing time and statistics
Batch Processing - multiple images at once

OCR Engine Comparison

OCR Engine	Installation	Speed*	Quality	Languages	Recommendation
Tesseract	System install	Fastest (0.5s)	Good	100+ languages	Best for speed
EasyOCR	`pip install`	Medium (2.3s)	Excellent	80+ languages	Best quality
PaddleOCR	`pip install`	Slower (4.2s)	Good	Multilingual	Balanced option

*Test conditions: Single image, CPU, resize_factor 0.5, languages: en+ru

Performance

NEW: Parallel Processing

The node now supports parallel processing of multiple images simultaneously!

Speedup: up to 8-10x when processing multiple images

// Example: 10 images processed in parallel
// Sequential: 20 seconds
// Parallel (4 threads): ~5 seconds

Recommended Settings

Scenario	Resize Factor	GPU	Mode	Speed	Quality
Maximum speed	0.3	Yes	Parallel	Very Fast	Medium
Balanced (recommended)	0.5	Yes	Parallel	Fast	Good
High quality	0.7	Yes	Batch	Medium	High
Critical quality	1.0	Yes	Sequential	Slow	Maximum

Recommendation: Use resize_factor: 0.5 with parallel processing for 8-10x speedup!

Installation

Method 1: Via Palette Manager (recommended)

Open Node-RED
Go to menu → Manage Palette
In the Install tab, search for @polina123/node-red-ocr-image-to-text
Click Install

Method 2: Via npm

cd ~/.node-red
npm install @polina123/node-red-ocr-image-to-text

Method 3: Manual installation for development

# Clone the repository
git clone https://github.com/polinaSuvorova/node_red_ocr_image_to_text.git
cd node_red_ocr_image_to_text

# Create symlink
npm link

# In Node-RED folder
cd ~/.node-red
npm link @polina123/node-red-ocr-image-to-text

Installing Python Dependencies

OCR engines will automatically install dependencies on first use. For manual installation:

# Core dependencies (required)
pip install opencv-python pillow

# PaddleOCR (recommended)
pip install paddlepaddle paddleocr

# EasyOCR
pip install easyocr

# Tesseract (requires system installation)
pip install pytesseract

# PDF Support (optional)
pip install PyPDF2 pdf2image
# Also requires poppler-utils system package

Installing PDF Support (Optional)

Python packages:

pip install PyPDF2 pdf2image

System requirements for pdf2image:

Windows:

Download poppler from https://github.com/oschwartz10612/poppler-windows/releases/
Extract and add to PATH

Linux (Ubuntu/Debian):

sudo apt install poppler-utils

macOS:

brew install poppler

Installing Tesseract OCR

Tesseract requires separate system installation:

Windows:

Download from https://github.com/UB-Mannheim/tesseract/wiki
Install and add to PATH

Linux (Ubuntu/Debian):

sudo apt install tesseract-ocr tesseract-ocr-rus tesseract-ocr-eng

macOS:

brew install tesseract

Usage

Input Data

The node expects a JSON object with an array of files:

{
  "files": [
    {
      "FILENAME": "document.jpg",
      "MIMETYPE": "image/jpeg",
      "FILEBASE64": "base64_encoded_image_data..."
    },
    {
      "FILENAME": "screenshot.png",
      "MIMETYPE": "image/png",
      "FILEBASE64": "base64_encoded_image_data..."
    },
    {
      "FILENAME": "scanned.pdf",
      "MIMETYPE": "application/pdf",
      "FILEBASE64": "base64_encoded_pdf_data..."
    }
  ]
}

Output Data

Image Output:

{
  "success": true,
  "results": [
    {
      "filename": "document.jpg",
      "mimetype": "image/jpeg",
      "status": "success",
      "text": "Recognized text from image combined into one line",
      "ocr_model": "paddleocr",
      "languages": ["en", "ru"],
      "resize_factor": 0.5,
      "processing_time": 1.23,
      "rotation_applied": 3.5,
      "error": null
    }
  ],
  "processed": 1,
  "total": 1,
  "errors": 0,
  "ocr_model": "paddleocr",
  "languages": ["en", "ru"],
  "resize_factor": 0.5,
  "performance": {
    "total_time": 1.45,
    "average_time_per_image": 1.23,
    "images_per_second": 0.81
  }
}

PDF Output:

{
  "success": true,
  "results": [
    {
      "filename": "document.pdf",
      "mimetype": "application/pdf",
      "status": "success",
      "text": "Extracted text from PDF...",
      "ocr_model": "tesseract",
      "languages": ["en", "ru"],
      "processing_time": 2.34,
      "pdf_info": {
        "pages": 5,
        "method": "text_layer",
        "pages_processed": 5
      },
      "error": null
    }
  ],
  "processed": 1,
  "total": 1,
  "errors": 0
}

Processing Statuses

Status	Description
`success`	Text successfully recognized
`no_text`	No text found in image
`error`	Error processing image
`unsupported_format`	Unsupported image format
`no_data`	No image data provided

Node Settings

Parameter	Description	Default
Name	Node name (optional)	-
OCR Model	Choose OCR engine: PaddleOCR, EasyOCR, Tesseract	`paddleocr`
Languages	Languages for recognition (multiple selection)	`en`, `ru`
Supported Formats	File formats to process: JPEG/JPG, PNG, PDF	`jpeg`, `png`
Resize Factor	Image resize coefficient (0.1-1.0)	`1.0`
Use GPU	Use GPU for acceleration (requires CUDA)	`false`
Auto-Rotate	Automatic text orientation detection and correction	`false`
Processing Mode	Sequential or Parallel processing	`sequential`
Max Threads	Number of parallel threads (1-32)	`4`

Supported Languages

English (en)
Russian (ru)
German (de)
French (fr)
Spanish (es)
And many more...

Usage Examples

Example 1: Fast processing with optimal settings

// Node configuration in Node-RED
{
  "id": "ocr-node",
  "type": "ocr-engine",
  "name": "Fast Recognition",
  "ocr_model": "paddleocr",
  "languages": ["en", "ru"],
  "resize_factor": 0.5,
  "gpu": true
}

Example 2: Preparing data for processing

// JavaScript function to prepare data
var files = [
  {
    FILENAME: "image1.jpg",
    MIMETYPE: "image/jpeg",
    FILEBASE64: msg.payload.base64data1
  },
  {
    FILENAME: "image2.png",
    MIMETYPE: "image/png",
    FILEBASE64: msg.payload.base64data2
  }
];

msg.payload = { files: files };
return msg;

Example 3: Processing results with performance metrics

// After OCR node
if (msg.payload.success) {
  node.log(`OCR engine: ${msg.payload.ocr_model}`);
  node.log(`Processed ${msg.payload.processed} of ${msg.payload.total} images`);
  node.log(`Total time: ${msg.payload.performance.total_time} sec`);
  node.log(`Speed: ${msg.payload.performance.images_per_second} img/sec`);

  msg.payload.results.forEach(function(result) {
    if (result.status === 'success') {
      node.log(`Text from ${result.filename}: ${result.text.substring(0, 100)}...`);
      node.log(`Processing time: ${result.processing_time} sec`);
    } else if (result.status === 'no_text') {
      node.warn(`No text found in ${result.filename}`);
    } else if (result.status === 'error') {
      node.error(`Error in ${result.filename}: ${result.error}`);
    }
  });
} else {
  node.error('OCR error: ' + msg.payload.error);
}

Requirements

Node.js >= 14.0.0
Node-RED >= 2.0.0
Python >= 3.7
Memory: ~2GB+ RAM (for recognition models)

Supported File Formats

JPEG/JPG (.jpg, .jpeg)
PNG (.png)
PDF (.pdf) - supports both text-based and scanned PDFs

Troubleshooting

Error: "Module not found"

# Reinstall Python dependencies
pip install --upgrade paddleocr easyocr pytesseract opencv-python pillow

Error: "GPU not available"

# Option 1: Install CUDA to use GPU
# Option 2: Disable "Use GPU" option in node settings

Slow Performance

Recommendations:

USE PARALLEL PROCESSING for multiple images
Reduce image size (resize_factor: 0.5)
Use GPU for acceleration (requires CUDA)
Use PaddleOCR instead of EasyOCR
Run performance test: npm run test:async

Tesseract not found

Windows:

Download and install: https://github.com/UB-Mannheim/tesseract/wiki
Add path to PATH

Linux:

sudo apt install tesseract-ocr tesseract-ocr-rus tesseract-ocr-eng

macOS:

brew install tesseract

Development

Project Structure

node_red_ocr_image_to_text/
├── nodes/
│   └── ocr-image-to-text/
│       ├── node_red_ocr_image_to_text.html    # UI configuration
│       ├── node_red_ocr_image_to_text.js      # Node.js logic
│       ├── node_red_ocr_image_to_text.py      # Python main script
│       ├── ocr_engine.py                      # Abstract base class
│       ├── easyocr_engine.py                  # EasyOCR implementation
│       ├── paddleocr_engine.py                # PaddleOCR implementation
│       ├── tesseract_engine.py                # Tesseract implementation
│       └── pdf_processor.py                   # PDF processing (NEW)
├── test/
│   ├── .mocharc.json                          # Mocha configuration
│   ├── sample_test_data.json                  # Test data
│   ├── test_ocr.py                            # Test script
│   ├── test_performance_comparison.py         # Performance comparison
│   ├── test_async_performance.py              # Parallel processing test
│   ├── test_model_switching.py                # Model switching test
│   ├── test_orientation.py                    # Orientation detection test
│   ├── test_pdf_ocr.py                        # PDF processing test
│   ├── node_spec.js                           # Node-RED unit tests
│   └── README.md                              # Testing documentation
├── documens_readme_md/
│   ├── PDF_SUPPORT.md                         # PDF feature documentation
│   ├── README_FOR_DEVELOPERS.md               # Developer guide (Russian)
│   └── PERFORMANCE.md                         # Performance optimization
├── logs_test/                                 # Test logs (in .gitignore)
│   └── performance_test_results_*.json        # Performance test results
├── .npmignore                                 # npm package exclusions
├── package.json
├── LICENSE
└── README.md

Local Development

Clone the repository
Install dependencies: npm install
Create symlink: npm link
In Node-RED folder: npm link @polina123/node-red-ocr-image-to-text
Restart Node-RED

Testing

# Basic test (requires test/sample_test_data.json)
npm run test:python

# Performance comparison of all engines
npm run test:performance

# Parallel vs Sequential processing test
npm run test:async

# Model switching test
npm run test:switching

# Text orientation detection test
npm run test:orientation

# PDF processing test
npm run test:pdf

# Node-RED unit tests
npm test

License

MIT License - see LICENSE file for details.

Support

If you have problems or questions:

Create Issue on GitHub
EasyOCR Documentation: https://github.com/JaidedAI/EasyOCR
PaddleOCR Documentation: https://github.com/PaddlePaddle/PaddleOCR
Tesseract Documentation: https://github.com/tesseract-ocr/tesseract

Acknowledgments

Based on EasyOCR by Jaided AI
Uses PaddleOCR by PaddlePaddle
Integrates Tesseract OCR by Google

Additional Information

Tesseract Architecture

Pytesseract is a Python wrapper for Tesseract OCR. Tesseract OCR is a powerful open-source library for optical character recognition (OCR), originally developed by HP and now maintained by Google.

How it works:

Python code → pytesseract → Tesseract OCR (system program) → result

Two-component system:

pytesseract (Python package) — installed via pip
tesseract (system program) — installed separately in OS

Author: Polina Suvorova Repository: https://github.com/polinaSuvorova/node_red_ocr_image_to_text

Node Info

Version: 1.0.5

Updated 15 hours ago

License: MIT

Rating: not yet rated

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Nodes

ocr-image-to-text

Keywords

node-red
easyocr
tesseract
paddleocr
ocr
text
image
pdf
recognition
multi-language
parallel
orientation
rotation
node-red
machine learning
ml

Maintainers

polina123

@polina123/node-red-ocr-image-to-text 1.0.5

Node-RED Image OCR (Multi-Engine)

Features

OCR Engine Comparison

Performance

NEW: Parallel Processing

Recommended Settings

Installation

Method 1: Via Palette Manager (recommended)

Method 2: Via npm

Method 3: Manual installation for development

Installing Python Dependencies

Installing PDF Support (Optional)

Installing Tesseract OCR

Usage

Input Data

Output Data

Processing Statuses

Node Settings

Supported Languages

Usage Examples

Example 1: Fast processing with optimal settings

Example 2: Preparing data for processing

Example 3: Processing results with performance metrics

Requirements

Supported File Formats

Troubleshooting

Error: "Module not found"

Error: "GPU not available"

Slow Performance

Tesseract not found

Development

Project Structure

Local Development

Testing

License

Support

Acknowledgments

Additional Information

Tesseract Architecture

Node Info

Categories

Actions

Downloads

Nodes

Keywords

Maintainers

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