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Zero Dharma Cultivation Project

🎯 Business Problem Solved

Reduced manual translation time by 90% (from 5 hours to 30 minutes for 100-page document)

📊 Key Metrics

  • Processing Speed: 5 seconds/page
  • Accuracy: 90% OCR accuracy
  • Languages Supported: 4

🛠️ Technologies & Skills Demonstrated

  • Python Development: OOP, error handling, testing
  • Data Analysis: Performance metrics, accuracy tracking
  • Project Management: Agile methodology, sprint planning
  • Quality Assurance: TDD, continuous improvement
  • Documentation: Technical writing, user guides

📈 Learning Outcomes

  • Applied Agile methodology to deliver DCP in 8 weeks
  • Improved OCR accuracy from 50% to 90% through iterative testing
  • Reduced processing time by 30% through optimization

📊 Version History

Universal Translator v1.5 - MVP

What It Does

Translates text in images from Chinese, Japanese, Korean, and Hindi to English with visual text replacement.

Quick Start

pip install -r requirements.txt
streamlit run app.py

Features

Visual translation (replaces text in images)
PDF support
ZIP batch processing
Web interface

Known Limitations

Simple white box overlay method
No font style matching
Basic layout preservation

Status

✅ MVP Complete - Ready for v2.0 AI enhancement

Developer

Victor - January 2026
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V 1.5 summary

Current Status:

✅ Working: Text detection (4/4 regions found), translation accuracy, file processing, UI
❌ Failing: Visual quality - original text not fully removed, English overlaying instead of replacing

Technical Issue:

OpenCV inpainting not removing text effectively. Result: English text overlaps Chinese instead of replacing it. Only 1/4 text regions properly processed.

Impact:

System functional but output quality too poor for production use. Core pipeline works (90% complete) but visual results unacceptable (40% quality).

Next Steps:

Need alternative to OpenCV inpainting - either better removal algorithm or simpler overlay approach with solid backgrounds.

Blocker: Text removal technology limitation


### v1.0 (Initial)
- GUI implementation with tkinter
- Basic OCR and translation
- Single language support
- Working MVP

### v1.1 (Week 1)
- **Platform**: Migrated to Jupyter Notebook
- **Architecture**: Class-based UniversalTranslator
- **Languages**: 5 language support
- **Enhancement**: Advanced image preprocessing
- **Corrections**: English text fix algorithms
- **Issues**: 58 style violations 

### v1.2 (Week 2) 
- **Quality**: Full PEP 8 compliance (0 errors)
- **Linting**: Ruff integration
- **Documentation**: Complete with type hints
- **Organization**: Clean project structure
- **Issues**: Needs batch function and eror handling

### v1.3 (Week 3) 
- **Code**: Enum-based language selection
- **Architecture**: Config class for centralized settings
- **Enhancement**: Error handling with retry mechanism
- **Organization**: Modular utilities
- **Corrections**: 290 Pylance errors 
- **Issues**: Needs batch function

v1.4 (Week 4)
- **Components**: FileHandler, PDFProcessor, ZIPProcessor, OutputGenerator
- **File Support**: PDF, ZIP, Images, Text (was images only)
- **Processing**: Batch file and archive processing
- **Output**: Multi-format generation (PDF/TXT/ZIP)
- **Quality**: Type hints fixed (any→Any), Path warnings ignored
- **Testing**: 100% component coverage, all passing
- **Issues**: Resolved - batch processing ✅, error handling ✅
- **Status**: Production ready