Rust PaddleOCR

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A lightweight and efficient OCR (Optical Character Recognition) Rust library based on PaddleOCR models. This library utilizes the MNN inference framework to provide high-performance text detection and recognition capabilities.

This project is a pure Rust library, focused on providing core OCR functionality. For command-line tools or HTTP services, please refer to:

• 🖥️ Command Line Tool: newbee-ocr-cli
• 🌐 HTTP Service: newbee-ocr-service

✨ Version 2.0 New Features

• 🎯 New Layered API Design: Provides a complete layered API ranging from low-level models to high-level Pipelines.
• 🔧 Flexible Model Loading: Supports loading models from file paths or memory bytes.
• ⚙️ Configurable Detection Parameters: Supports custom detection thresholds, resolution, precision modes, and more.
• 🎚️ Three Precision Presets: Fast, Balanced, and High-Precision modes to meet different scenario requirements.
• 🚀 GPU Acceleration Support: Supports multiple GPU backends including Metal, OpenCL, and Vulkan.
• 📦 Batch Processing Optimization: Supports batch text recognition to improve throughput.
• 🔌 Independent Engine Mode: Ability to create standalone detection engines or recognition engines.

Features

Core Capabilities

• Text Detection: Accurately locates text regions within images.
• Text Recognition: Recognizes text content within detected regions.
• End-to-End Recognition: completes the full flow of detection and recognition in a single call.
• Layered API Architecture: Supports three usage patterns: end-to-end, layered calls, and independent models.

Model Support

• Multi-Version Support: Supports both PP-OCRv4 and PP-OCRv5 models for flexible selection.
• Multi-Language Support: PP-OCRv5 supports 11+ specific language models, covering over 100 languages.
• Complex Scenario Recognition: Enhanced capabilities for handwritten text, vertical text, and rare characters.
• Flexible Loading: Models can be loaded via file paths or directly from memory bytes.

Performance

• High-Performance Inference: Based on the MNN inference framework for fast speed and low memory usage.
• GPU Acceleration: Supports Metal, OpenCL, Vulkan, and other GPU backends.
• Batch Processing: Supports batch text recognition to boost throughput.
• Precision Presets: Provides Fast, Balanced, and High-Precision modes.

Developer Experience

• Flexible Configuration: Parameters such as detection thresholds, resolution, and precision modes are customizable.
• Memory Safety: Automatic memory management to prevent leaks.
• Pure Rust Implementation: No external runtime required, cross-platform compatible.
• Minimal Dependencies: Lightweight and easy to integrate.

Model Versions

This library supports three versions of PaddleOCR models:

PP-OCRv4

• Stable Version: thoroughly verified with excellent compatibility.
• Use Case: Standard document recognition where high accuracy is required.
• Model Files:
  • Detection: ch_PP-OCRv4_det_infer.mnn
  • Recognition: ch_PP-OCRv4_rec_infer.mnn
  • Charset: ppocr_keys_v4.txt

PP-OCRv5

• Latest Version: The next-generation text recognition solution.
• Multi-Language Support: The default model (PP-OCRv5_mobile_rec.mnn) supports Simplified Chinese, Traditional Chinese, English, Japanese, and Chinese Pinyin.
• Specific Language Models: Provides dedicated models for 11+ languages covering 100+ languages for optimal performance.
• Shared Detection Model: All V5 language models share the same detection model (PP-OCRv5_mobile_det.mnn).
• Enhanced Scenario Recognition:
  • Significantly improved recognition for complex handwritten Chinese/English.
  • Optimized vertical text recognition.
  • Enhanced recognition of rare characters.
• Performance: 13% improvement in end-to-end performance compared to PP-OCRv4.
• Model Files (Default Multi-language):
  • Detection: PP-OCRv5_mobile_det.mnn (Shared by all languages)
  • Recognition: PP-OCRv5_mobile_rec.mnn (Default, supports CN/EN/JP)
  • Charset: ppocr_keys_v5.txt
• Specific Language Model Files (Optional):
  • Recognition: {lang}_PP-OCRv5_mobile_rec_infer.mnn
  • Charset: ppocr_keys_{lang}.txt
  • Available Language Codes: arabic, cyrillic, devanagari, el, en, eslav, korean, latin, ta, te, th

PP-OCRv5 Language Model Support List

PP-OCRv5 FP16

• High Efficiency Version: Provides faster inference speeds and lower memory usage without sacrificing accuracy.
• Use Case: Scenarios requiring high performance and low memory footprint.
• Performance Gains:
  • Inference speed increased by ~9% (higher on devices supporting FP16 acceleration).
  • Memory usage reduced by ~8%.
  • Model size halved.
• Model Files:
  • Detection: PP-OCRv5_mobile_det_fp16.mnn
  • Recognition: PP-OCRv5_mobile_rec_fp16.mnn
  • Charset: ppocr_keys_v5.txt

Model Performance Comparison

Application Scenarios

Choose the appropriate API level based on your requirements:

Scenario 1: Quick OCR Integration

Use: End-to-End Recognition (OcrEngine)

Suitable for:

• Rapid prototyping.
• Simple document recognition needs.
• No need for intermediate processing.
• Only care about the final text result.

let engine = OcrEngine::new(det_path, rec_path, charset_path, None)?;
let results = engine.recognize(&image)?;

Scenario 2: Custom Post-Processing for Detection

Use: Layered Calls (OcrEngine detect + recognize_batch)

Suitable for:

• Filtering or selecting detection results.
• Adjusting text box positions.
• Processing text in a specific order.
• Sorting or grouping detection boxes.

let engine = OcrEngine::new(det_path, rec_path, charset_path, None)?;
// 1. Detect
let mut boxes = engine.detect(&image)?;
// 2. Custom processing (e.g., filter small boxes)
boxes.retain(|b| b.rect.width() > 50);
// 3. Recognize
let detections = engine.det_model().detect_and_crop(&image)?;
let results = engine.recognize_batch(&images)?;

Scenario 3: Detection Only

Use: DetOnlyEngine

Suitable for:

• Document layout analysis.
• Text region annotation tools.
• Pre-processing workflows (only need text locations).
• Using with other recognition engines.

let det_engine = OcrEngine::det_only("models/det_model.mnn", None)?;
let text_boxes = det_engine.detect(&image)?;
// Use detection boxes for other processing...

Scenario 4: Recognition Only

Use: RecOnlyEngine

Suitable for:

• Text location is already known, only recognition is needed.
• Processing pre-cropped text line images.
• Handwriting recognition (input is a single line image).
• Batch recognition of fixed-format text.

let rec_engine = OcrEngine::rec_only(
    "models/rec_model.mnn",
    "models/ppocr_keys.txt",
    None
)?;
let text = rec_engine.recognize_text(&text_line_image)?;

Scenario 5: Fully Custom Workflow

Use: Independent Models (DetModel + RecModel)

Suitable for:

• Custom pre-processing workflows.
• Different configurations for detection and recognition.
• Inserting complex logic between detection and recognition.
• Performance optimization (e.g., reusing detection results).

let det_model = DetModel::from_file("models/det_model.mnn", None)?
    .with_options(DetOptions::high_precision());
    
let rec_model = RecModel::from_file(
    "models/rec_model.mnn",
    "models/ppocr_keys.txt",
    None
)?;

// Fully custom processing flow...

Scenario 6: Embedded or Encrypted Deployment

Use: Load from Bytes

Suitable for:

• Embedded devices (compiling models into the binary).
• Model encryption requirements.
• Downloading models dynamically from the network.
• Custom model storage formats.

let det_bytes = include_bytes!("../models/det_model.mnn");
let rec_bytes = include_bytes!("../models/rec_model.mnn");
let charset_bytes = include_bytes!("../models/ppocr_keys.txt");

let engine = OcrEngine::from_bytes(det_bytes, rec_bytes, charset_bytes, None)?;

Installation

Add the following to your Cargo.toml:

[dependencies.rust-paddle-ocr]
git = "https://github.com/zibo-chen/rust-paddle-ocr.git"

You can also specify a specific branch or tag:

[dependencies.rust-paddle-ocr]
git = "https://github.com/zibo-chen/rust-paddle-ocr.git"
branch = "next"

Prerequisites

This library requires:

• Pre-trained PaddleOCR models converted to MNN format.
• A character set file for text recognition.

API Architecture

This library provides a Layered Inference API, allowing you to choose the usage pattern that best fits your scenario:

┌─────────────────────────────────────────────────┐
│         OcrEngine (End-to-End Pipeline)         │
│      Complete detection & recognition in one call │
├─────────────────────────────────────────────────┤
│  DetOnlyEngine  │  RecOnlyEngine   │  OcrEngine │
│  Detection Only │ Recognition Only │  Det + Rec │
├─────────────────────────────────────────────────┤
│     DetModel          │        RecModel         │
│   Text Det Model      │      Text Rec Model     │
├─────────────────────────────────────────────────┤
│            InferenceEngine (MNN)                │
│            Low-level Inference Engine           │
└─────────────────────────────────────────────────┘

Three Usage Patterns

1. End-to-End Recognition (Recommended) - Simplest

Use OcrEngine to complete the full OCR process with a single call:

use ocr_rs::OcrEngine;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create OCR engine (using default config)
    let engine = OcrEngine::new(
        "models/PP-OCRv5_mobile_det.mnn",
        "models/PP-OCRv5_mobile_rec.mnn",
        "models/ppocr_keys_v5.txt",
        None,
    )?;
    
    // Load image
    let image = image::open("test.jpg")?;
    
    // Perform detection and recognition in one call
    let results = engine.recognize(&image)?;
    
    // Output results
    for result in results {
        println!("Text: {}", result.text);
        println!("Confidence: {:.2}%", result.confidence * 100.0);
        println!("Position: ({}, {})", result.bbox.rect.left(), result.bbox.rect.top());
    }
    
    Ok(())
}

2. Layered Calls - More Flexible

Use OcrEngine but call detection and recognition separately. Useful for inserting custom processing:

use ocr_rs::OcrEngine;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let engine = OcrEngine::new(det_path, rec_path, charset_path, None)?;
    let image = image::open("test.jpg")?;
    
    // 1. Detection only first
    let text_boxes = engine.detect(&image)?;
    println!("Detected {} text regions", text_boxes.len());
    
    // Custom processing can be done here, e.g.:
    // - Filter unwanted regions
    // - Adjust box positions
    // - Sort by position, etc.
    
    // 2. Get detection model and manually crop
    let det_model = engine.det_model();
    let detections = det_model.detect_and_crop(&image)?;
    
    // 3. Batch recognize cropped images
    let cropped_images: Vec<_> = detections.iter()
        .map(|(img, _)| img.clone())
        .collect();
    let rec_results = engine.recognize_batch(&cropped_images)?;
    
    for (result, (_, bbox)) in rec_results.iter().zip(detections.iter()) {
        println!("{}: {:.2}%", result.text, result.confidence * 100.0);
    }
    
    Ok(())
}

3. Independent Model Calls - Most Flexible

Create detection and recognition engines separately, or create a single-function engine:

use ocr_rs::{DetModel, RecModel, DetOptions, RecOptions};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Method A: Create detection and recognition models separately
    let det_model = DetModel::from_file("models/det_model.mnn", None)?
        .with_options(DetOptions::high_precision());
    
    let rec_model = RecModel::from_file(
        "models/rec_model.mnn",
        "models/ppocr_keys.txt",
        None
    )?.with_options(RecOptions::new().with_min_score(0.5));
    
    let image = image::open("test.jpg")?;
    
    // Detect and crop
    let detections = det_model.detect_and_crop(&image)?;
    
    // Batch recognize
    let images: Vec<_> = detections.iter().map(|(img, _)| img.clone()).collect();
    let results = rec_model.recognize_batch(&images)?;
    
    // Process results...
    
    // Method B: Create detection-only engine
    let det_only = OcrEngine::det_only("models/det_model.mnn", None)?;
    let text_boxes = det_only.detect(&image)?;
    
    // Method C: Create recognition-only engine
    let rec_only = OcrEngine::rec_only(
        "models/rec_model.mnn",
        "models/ppocr_keys.txt",
        None
    )?;
    let text = rec_only.recognize_text(&cropped_image)?;
    
    Ok(())
}

Usage Examples

Basic Configuration Options

use ocr_rs::{OcrEngine, OcrEngineConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Use precision presets
    let config = OcrEngineConfig::fast();        // Fast mode
    // let config = OcrEngineConfig::balanced();    // Balanced mode (Default)
    // let config = OcrEngineConfig::high_precision(); // High precision mode
    
    let engine = OcrEngine::new(
        "models/PP-OCRv5_mobile_det.mnn",
        "models/PP-OCRv5_mobile_rec.mnn",
        "models/ppocr_keys_v5.txt",
        Some(config),
    )?;
    
    let image = image::open("test.jpg")?;
    let results = engine.recognize(&image)?;
    
    Ok(())
}

GPU Acceleration

use ocr_rs::{OcrEngine, OcrEngineConfig, Backend};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Use GPU acceleration
    let config = OcrEngineConfig::new()
        .with_backend(Backend::Metal);    // macOS: Metal
        // .with_backend(Backend::OpenCL); // Cross-platform: OpenCL
        // .with_backend(Backend::Vulkan); // Windows/Linux: Vulkan
    
    let engine = OcrEngine::new(det_path, rec_path, charset_path, Some(config))?;
    
    Ok(())
}

Custom Detection and Recognition Parameters

use ocr_rs::{OcrEngine, OcrEngineConfig, DetOptions, RecOptions};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Custom configuration
    let config = OcrEngineConfig::new()
        .with_threads(8)
        .with_det_options(
            DetOptions::new()
                .with_max_side_len(1920)     // Higher detection resolution
                .with_box_threshold(0.6)     // Stricter bounding box threshold
                .with_merge_boxes(true)      // Merge adjacent text boxes
        )
        .with_rec_options(
            RecOptions::new()
                .with_min_score(0.5)         // Filter low confidence results
                .with_batch_size(16)         // Batch recognition size
        );
    
    let engine = OcrEngine::new(det_path, rec_path, charset_path, Some(config))?;
    
    Ok(())
}

Using Specific Language Models

use ocr_rs::OcrEngine;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Use Korean model
    let engine = OcrEngine::new(
        "models/PP-OCRv5_mobile_det.mnn",
        "models/korean_PP-OCRv5_mobile_rec_infer.mnn",
        "models/ppocr_keys_korean.txt",
        None,
    )?;
    
    let image = image::open("korean_text.jpg")?;
    let results = engine.recognize(&image)?;
    
    for result in results {
        println!("{}: {:.2}%", result.text, result.confidence * 100.0);
    }
    
    Ok(())
}

Loading Models from Memory Bytes

Suitable for embedded deployment or scenarios requiring model encryption:

use ocr_rs::OcrEngine;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Read model bytes from file (or other sources)
    let det_bytes = std::fs::read("models/det_model.mnn")?;
    let rec_bytes = std::fs::read("models/rec_model.mnn")?;
    let charset_bytes = std::fs::read("models/ppocr_keys.txt")?;
    
    // Create engine from bytes
    let engine = OcrEngine::from_bytes(
        &det_bytes,
        &rec_bytes,
        &charset_bytes,
        None,
    )?;
    
    let image = image::open("test.jpg")?;
    let results = engine.recognize(&image)?;
    
    Ok(())
}

Convenience Functions

use ocr_rs::ocr_file;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Perform OCR in one line of code
    let results = ocr_file(
        "test.jpg",
        "models/det_model.mnn",
        "models/rec_model.mnn",
        "models/ppocr_keys.txt",
    )?;
    
    for result in results {
        println!("{}", result.text);
    }
    
    Ok(())
}

For more complete examples, please refer to the examples directory.

Related Projects

• 🖥️ newbee-ocr-cli - A command-line tool based on this library, providing a simple and easy-to-use OCR CLI interface.
• 🌐 newbee-ocr-service - An HTTP service based on this library, providing RESTful API interfaces.

Performance Optimization Suggestions

1. Choose Appropriate Precision Modes

// Real-time processing
let config = OcrEngineConfig::fast();

// General document recognition
let config = OcrEngineConfig::balanced();

// High quality requirements
let config = OcrEngineConfig::high_precision();

2. Use GPU Acceleration

// macOS/iOS
let config = OcrEngineConfig::gpu();  // Uses Metal

// Other platforms
let config = OcrEngineConfig::new().with_backend(Backend::OpenCL);

3. Batch Processing

// Batch recognizing multiple text lines is much faster than one by one
let results = rec_model.recognize_batch(&images)?;

Contribution

Contributions are welcome! Please feel free to submit Issues or Pull Requests.

License

This project is licensed under the Apache License, Version 2.0 - see the LICENSE file for details.

Acknowledgements

• PaddleOCR - Provided the original OCR models and research.
• MNN - Provided the efficient neural network inference framework.