super-ocr（超级 OCR）

Super OCR

Overview

Super OCR is a production-grade optical character recognition tool that intelligently selects the best engine for your needs:

• Tesseract Engine: Lightweight, fast (~200-500ms), perfect for simple text extraction
• PaddleOCR Engine: High accuracy (98%+), optimized for Chinese, ideal for complex documents

Engine Selection Strategy

Auto Mode (Default)

The skill automatically selects the optimal engine:

Force Mode

Users can explicitly choose an engine:

• --engine tesseract - Use Tesseract only
• --engine paddle - Use PaddleOCR only
• --engine auto - Auto-select (default)

Quick Start

Installation

This skill requires the following dependencies:

• PaddleOCR (for Chinese text recognition - 98%+ accuracy)
• Tesseract (for fast English text recognition)
• OpenCV (for image preprocessing)

Option 1: Install with pip (all-in-one)

pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy

Option 2: Install dependencies manually

macOS:

# Tesseract
brew install tesseract

# PaddleOCR
pip install paddleocr paddlepaddle

Ubuntu/Debian:

# Tesseract
sudo apt update && sudo apt install tesseract-ocr

# PaddleOCR
pip install paddleocr paddlepaddle

Windows:

# Download Tesseract from: https://github.com/UB-Mannheim/tesseract/wiki
pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy

Usage

# Auto mode (recommended) - runs all available engines
cd path/to/super-ocr
python scripts/main.py --image path/to/image.png

# Force Tesseract only
python scripts/main.py --image document.jpg --engine tesseract

# Force PaddleOCR (high accuracy Chinese)
python scripts/main.py --image chinese_menu.png --engine paddle

# Run all engines (macOS only: Tesseract + PaddleOCR + MacVision)
python scripts/main.py --image complex_doc.png --engine all

# Batch processing with output directory
python scripts/main.py --images ./images/*.png --output ./results --verbose

# Check dependencies and auto-install
python scripts/dependencies.py --check --install

Structuring This Skill

This skill uses a capabilities-based structure with multiple execution modes:

1. Engine Selection Logic - Intelligent decision making
2. OCR Execution - Unified interface for different engines
3. Post-processing - Standardized output formatting
4. Validation & Fallback - Quality assurance

Core Capabilities

1. Intelligent Engine Selection

The skill includes a decision tree that analyzes:

• Image characteristics (contrast, text size)
• Language patterns (Chinese character detection)
• User requirements (speed vs accuracy)

See scripts/engine_selector.py for implementation details.

2. Dual Engine Support

Tesseract Engine (scripts/tesseract_ocr.py):

• Fast preprocessing pipeline
• PSM mode 6 for uniform text blocks
• Confidence scoring per word
• Language detection

PaddleOCR Engine (scripts/paddle_ocr.py):

• State-of-art? SN (East text detection)
• Crnn recognition with LSTM
• Confidence scores per character
• Table detection support

3. Output Formats

Supports multiple output formats:

4. Quality Guarantees

• Confidence thresholds (configurable, default 80%)
• Low-confidence alerts for manual review
• \Fallback processing for failed OCRs

Resources

scripts/

• main.py - Main entry point, CLI interface (supports multi-engine)
• dependencies.py - Auto-install and validation
• output_formatter.py - Multiple output format support
• engine/ - OCR engine implementations

- selector.py - Intelligent engine selection logic - tesseract.py - Tesseract engine wrapper - paddle.py - PaddleOCR engine wrapper - macvision.py - macOS Vision OCR (macOS only)

• preprocessing/ - Image preprocessing utilities

- preprocessor.py - Denoising, enhancement, binarization

dependencies.py (Key Feature)

The dependencies.py module handles:

• Dependency detection (paddleocr, paddlepaddle, pytesseract, cv2)
• Auto-install on missing dependencies
• version checking
• OS-specific installation commands
• Clear error messages with troubleshooting steps

Use this when setting up a new environment with python scripts/dependencies.py --check --install

Advanced Features

Custom Configuration

Create config.yaml for persistent settings:

default_engine: auto
confidence_threshold: 0.8
output_format: json
preprocess:
  denoise: true
  enhance_contrast: true

Batch Processing

Process multiple images:

python scripts/ocr.py --images ./images/*.png --output ./results

API Mode

Use as a Python library:

from super_ocr import OCRProcessor

processor = OCRProcessor(engine='auto')
result = processor.extract('image.png')
print(result.text)
print(result.confidence)

Anti-Patterns

• ❌ Using PaddleOCR for every image (overhead for simple cases)
• ❌ ignoring confidence scores (quality matters)
• ❌ Biases (always prefering one engine)
• ❌ Skipping preprocessing (quality impact)

Performance Notes

Initialize once, reuse processor for batch processing.