⚠️ LEGACY NOTICE: This repository is in maintenance mode. Active development has moved to omni-medical-suite. See LEGACY_NOTICE.md for the full migration plan.

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title: OmniFile AI Processor emoji: 🧠 colorFrom: blue colorTo: green sdk: docker app_port: 7860 pinned: false license: mit

🧠 OmniFile AI Processor v5.0.0

نظام ذكاء اصطناعي متكامل لمعالجة الملفات والنصوص والخط اليدوي A Comprehensive AI System for File Processing, Text Analysis & Handwriting Recognition

Version: v5.0.0  |  Status: ✅ CI-Verified

🌐 Live Demo (HF Spaces)  |  🧪 HF Lab Space  |  📘 Documentation  |  🧩 Dependency Profiles  |  📓 Colab Debug Notebook  |  🗂️ Prioritized Suggestions  |  🐛 Report Bug  |  💡 Suggestions

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👨‍💻 About the Author | عن المؤلف

Dr Abdulmalek Tamer Al-husseini 📍 Location: Homs, Syria 📧 Email: Abdulmalek.husseini@gmail.com 🐙 GitHub: DrAbdulmalek 🤗 HuggingFace: DrAbdulmalek

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📖 Description | الوصف

OmniFile AI Processor is a production-ready, multimodal AI system that integrates six projects into a unified platform for document intelligence:

OmniFile_Processor + HandwrittenOCR + handwriting-ocr + arabic-ocr-pro + advanced-ocr + OCR-Enhancer

نظام ذكاء اصطناعي متقدم يجمع ستة مشاريع في منصة واحدة لمعالجة الملفات والخط اليدوي. يدعم العربية والإنجليزية والألمانية مع وحدات متخصصة للرؤية الحاسوبية ومعالجة اللغة والأمان والتصدير.

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✨ Features | المميزات

🔍 Computer Vision & OCR (وحدة الرؤية الحاسوبية)

1. Multi-Engine OCR — 4 engines (TrOCR, EasyOCR, Tesseract, PaddleOCR) with intelligent engine selection
2. Result Fusion — 4 strategies: highest confidence, weighted average, voting, longest text
3. Advanced Preprocessing — CLAHE, deskew, denoise, Otsu thresholding, ONNX Runtime acceleration
4. Layout Analysis — Automatic detection of tables, headers, footers, and document structure
5. Table Extraction — Hough line detection + contour analysis for structured data extraction

🗣️ Natural Language Processing (وحدة معالجة اللغة)

6. Multilingual Spell Correction — Arabic, English, German with user-learning capability (186+ Arabic corrections)
7. RTL Text Processing — Full Arabic reshaping + BiDi support with 40+ normalization mappings
8. Mixed-Text Handling — Arabic/English/numbers with medical term protection
9. Translation Engine — Helsinki-NLP/opus-mt supporting 6 language pairs
10. AI Summarization — BART (facebook/bart-large-cnn) + Arabic (UAE-Code/mbart-summarization-ar)
11. Entity Extraction & Text Classification — BERT-based NER with 6-category classification

🤖 AI Enhancement (وحدة الذكاء الاصطناعي)

12. GPT & Gemini Refinement — Context-aware OCR correction with block-type-specific prompts
13. SSIM Pattern Matching — Self-learning from corrected word images with SQLite pattern database

🌐 AI Gateway (وحدة بوابة الذكاء الاصطناعي)

14. Universal AI Model Proxy — Drop-in proxy server compatible with Messages API protocol, routes to 8+ providers (DeepSeek, NVIDIA NIM, OpenRouter, Ollama, LM Studio, llama.cpp, Kimi, Wafer)
15. Multi-Provider Routing — Intelligent model routing with per-tier selection (Opus/Sonnet/Haiku levels) and automatic fallback
16. Streaming SSE — Full Server-Sent Events streaming with thinking block support and tool call conversion
17. Request Optimization — Fast-path optimizations including quota mock, prefix detection, title/suggestion skip
18. Rate Limiting & Concurrency — Configurable per-provider rate limits, concurrency control, and timeout management

📝 HTR Training System (نظام تدريب التعرف على الخط)

29. Arabic HTR Pipeline — End-to-end Arabic handwritten text recognition: LineSegmenter → TrOCR → DottedRecovery
30. LoRA Fine-Tuning — Efficient TrOCR fine-tuning with PEFT/LoRA (4-8 GB VRAM) for Arabic handwriting
31. Active Learning — Uncertainty/Diversity/Hybrid sampling to select informative samples for annotation
32. Synthetic Data Generation — Arabic handwriting synthesis with font variation and degradation simulation
33. Mobile Review Integration — Bidirectional sync between training pipeline and mobile review interface

📤 Multi-Format Export (وحدة التصدير)

19. 6 Export Formats — DOCX (RTL support), HTML, searchable PDF, Excel, JSON (with BBox), TXT (UTF-8 BOM)

🔒 Security & Privacy (وحدة الأمان)

20. PII Detection — Presidio-based sensitive data scanning + detect-secrets
21. File Encryption — Fernet (AES-128) with folder support
22. Code Protection — Prevents spell correction inside code blocks
23. Audit Logging — File + Redis audit trail with rate limiting (slowapi + Nginx)

📊 Evaluation (وحدة التقييم)

24. CER/WER Metrics — OCR accuracy evaluation with Arabic normalization + Levenshtein distance
25. Quality Grading — A+ to F with actionable recommendations

🖥️ Multiple Interfaces (واجهات المستخدم)

26. 4 UIs — Streamlit (6 tabs), Gradio (7 tabs), React + shadcn/ui (dark/light), CLI, PyQt6 desktop
27. FastAPI Backend — Full REST API with Swagger documentation

🚀 Scalability & Deployment (التحجيم والنشر)

28. Docker + Compose — One-command deployment with all services
29. Kubernetes Ready — Complete K8s manifests with HPA (2-10 pods auto-scaling)
30. Celery + Redis — Asynchronous task processing for heavy workloads

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🚀 Quick Start | التشغيل السريع

Option 1: HuggingFace Spaces (Recommended for Demo)

The project is deployed and available at: 👉 Production demo: https://huggingface.co/spaces/DrAbdulmalek/handwriting-ocr

👉 Experimental lab space: https://huggingface.co/spaces/DrAbdulmalek/omnifile-processor-lab

To deploy your own instance:

git clone https://github.com/DrAbdulmalek/OmniFile_Processor.git
cd OmniFile_Processor
pip install -r requirements-hf.txt
python -m src.gradio_ui

Option 2: Local Installation (Linux / macOS / Windows)

# Clone the repository
git clone https://github.com/DrAbdulmalek/OmniFile_Processor.git
cd OmniFile_Processor

# Install dependencies
pip install -r requirements-full.txt          # Everything (~6-8 GB, ~15-30 min)

# Or install in layers:
# pip install -r requirements-core.txt          # Minimum (~1.5 GB, ~3 min)
# pip install -r requirements-core.txt -r requirements-ocr.txt   # + OCR engines
# pip install -r requirements-core.txt -r requirements-nlp.txt   # + NLP

# Run with your preferred interface
streamlit run app.py          # Streamlit UI (6 tabs)
python -m src.gradio_ui       # Gradio UI (7 tabs)
python main.py                # CLI interface
cd frontend && npm install && npm run dev  # React Frontend

Option 3: Docker Compose (Full Stack)

git clone https://github.com/DrAbdulmalek/OmniFile_Processor.git
cd OmniFile_Processor
docker-compose up -d

# Access:
# API Docs:    http://localhost:5001/docs
# Streamlit:   http://localhost:7860
# React:       http://localhost:3000
# Nginx Proxy: http://localhost

Option 3.5: Mobile Review → Training Data

python mobile_review/server.py --host 0.0.0.0 --port 5000
# بعد المراجعة والحفظ:
python mobile_review/server.py --export-dataset --export-output mobile_review/dataset/review_dataset

# أو عبر الأداة مباشرة:
python tools/build_training_data.py --corrections mobile_review/ocr_corrected.json --output dataset/review_dataset

Option 4: Google Colab

يفضَّل استخدام الدفتر الجاهز: 👉 notebooks/OmniFile_Processor_Colab_Debug.ipynb

!git clone https://github.com/DrAbdulmalek/OmniFile_Processor.git
%cd OmniFile_Processor
!apt-get update -qq
!apt-get install -y -qq poppler-utils tesseract-ocr tesseract-ocr-ara tesseract-ocr-eng libgl1
!pip install -r requirements-colab.txt
!python hf_app.py

للمزيد عن فروق التثبيت، راجع docs/DEPENDENCY_PROFILES.md.

Option 5: AI Gateway (Universal Model Proxy)

The OmniFile AI Gateway provides a universal proxy for routing AI model requests to multiple providers:

# Clone and install gateway dependencies
git clone https://github.com/DrAbdulmalek/OmniFile_Processor.git
cd OmniFile_Processor
pip install -r requirements-gateway.txt

# Configure your provider (edit modules/ai/gateway/.env)
cp modules/ai/gateway/.env.example modules/ai/gateway/.env

# Start the gateway
bash scripts/start_gateway.sh

# Or start manually:
python -m uvicorn modules.ai.gateway.server:app --host 0.0.0.0 --port 8082

Supported Providers: DeepSeek, NVIDIA NIM, OpenRouter, Ollama, LM Studio, llama.cpp, Kimi, Wafer

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🧪 Benchmark & Review Utilities

# Benchmark OCR engines on a folder of images
python tools/benchmark_ocr.py --images examples/ --output artifacts/ocr_benchmark

# Optional ground truth comparison
python tools/benchmark_ocr.py --images examples/ --ground-truth examples/ground_truth.json --output artifacts/ocr_benchmark

📁 Project Structure | هيكل المشروع

ملاحظة معمارية — Architecture Note: يوجد في المشروع نظامان متوازيان:

• modules/ — البنية النظرية الموسّعة: وحدات منظّمة بوضوح (vision, nlp, security, export, ai, evaluation) مع نماذج Pydantic v2. هذه هي البنية المستقبلية المقصودة للمشروع.
• src/ — محرك HandwrittenOCR العملي: يحتوي على التطبيق الفعلي المُستخدَم في واجهة Gradio (src/gradio_ui.py) وHF Spaces. يشمل TrOCR Batch, LoRA Fine-tuning, Active Learning, وStudy Guide.
• الملفات الجذرية (app.py, database.py, config.py) — طبقة التكامل التي تربط بين البنية والمحركات.

الخيار المتبنّى حالياً: src/ هو الكود العملي الفعّال لواجهة Gradio وHF Spaces، بينما modules/ يمثل البنية النظرية المنظمة للمشروع الموسّع. التحويل التدريجي (migration) من src/ إلى modules/ سيتم على مراحل عبر Pull Requests مستقلة.

OmniFile_Processor/
├── app.py                          # Main Streamlit UI
├── config.py                       # Central configuration v4.1.1
├── database.py                     # SQLite database layer
├── main.py                         # Local / CLI entry point
├── tasks.py                        # Celery async tasks
├── requirements.txt                # Full dependencies (legacy)
├── requirements-core.txt           # Core only (~1.5 GB)
├── requirements-ocr.txt            # OCR engines layer
├── requirements-nlp.txt            # NLP layer
├── requirements-full.txt           # Everything (~6-8 GB)
├── requirements-hf.txt             # HuggingFace Spaces (minimal)
├── Dockerfile                      # Docker image
├── docker-compose.yml              # Full stack orchestration
├── nginx.conf                      # Nginx load balancer
├── LICENSE                         # MIT License
│
├── modules/
│   ├── core/                       # Core data models
│   │   └── structure.py            #   Pydantic v2 models
│   │
│   ├── vision/                     # Computer Vision & OCR
│   │   ├── ocr_engine.py           #   4 OCR engines + ONNX + Quantization
│   │   ├── image_preprocessor.py   #   CLAHE + Denoise + Deskew + Otsu
│   │   ├── pdf_processor.py        #   Multi-format PDF processing
│   │   ├── text_reconstructor.py   #   RTL/LTR sentence reconstruction
│   │   ├── result_fusion.py        #   4 fusion strategies
│   │   ├── layout_analyzer.py      #   Layout analysis (tables, headers)
│   │   ├── table_extractor.py      #   Table extraction (Hough + contours)
│   │   └── htr/                    #   Arabic HTR Module
│   │       ├── arabic_htr.py       #     Unified HTR pipeline
│   │       ├── line_segmenter.py   #     Document→lines (projection/contour/U-Net)
│   │       ├── word_segmenter.py   #     Lines→words (connected components)
│   │       ├── dotted_recovery.py  #     Arabic dot correction
│   │       └── trocr_finetuned.py  #     Fine-tuned TrOCR wrapper
│   │
│   ├── nlp/                        # Natural Language Processing
│   │   ├── spell_corrector.py      #   3-language correction + learning
│   │   ├── translator.py           #   Helsinki-NLP translation
│   │   ├── summarizer.py           #   BART summarization
│   │   ├── entity_extractor.py     #   BERT-based NER
│   │   ├── language_detector.py    #   Language detection
│   │   ├── text_classifier.py      #   6-category classification
│   │   ├── arabic_rtl.py           #   Full RTL processing
│   │   ├── mixed_text.py           #   Arabic/English mixed text
│   │   ├── ai_corrector.py         #   GPT-based correction
│   │   ├── arabic_nlp_utils.py     #   Semantic similarity for Arabic OCR
│   │   └── correction_dict.json    #   186+ Arabic corrections
│   │
│   ├── ai/                         # AI Enhancement
│   │   ├── pattern_matcher.py      #   SSIM pattern matching
│   │   ├── pattern_db.py           #   SQLite pattern database
│   │   ├── gemini_refiner.py       #   Gemini AI refinement
│   │   └── gateway/                #   OmniFile AI Gateway (universal proxy)
│   │       ├── server.py           #     ASGI entry point
│   │       ├── api/                #     FastAPI routes & auth
│   │       ├── config/             #     Provider catalog & settings
│   │       ├── core/               #     SSE & protocol helpers
│   │       ├── providers/          #     8 provider backends
│   │       └── pool/               #     Account pool & health management
│   │
│   ├── security/                   # Security & Privacy
│   │   ├── file_scanner.py         #   Security scanning
│   │   ├── sensitive_data_scanner.py # PII detection (Presidio)
│   │   ├── encryption.py           #   Fernet encryption (AES-128)
│   │   ├── code_protector.py       #   Code block protection
│   │   ├── file_organizer.py       #   Auto file organization
│   │   ├── archive_handler.py      #   Archive management
│   │   ├── backup_manager.py       #   Backup management
│   │   ├── audit_logger.py         #   Audit logging
│   │   └── secure_file_handler.py  #   Safe file handling
│   │
│   ├── export/                     # Multi-Format Export
│   │   ├── exporter.py             #   DOCX/HTML/PDF/JSON/TXT/Excel
│   │   └── layout_preserving.py    #   DOCX export with visual layout preservation
│   │
│   └── evaluation/                 # Evaluation & Metrics
│       └── metrics.py              #   CER/WER + quality grading
│
├── frontend/                       # React + shadcn/ui Web App
│   ├── src/
│   │   ├── App.jsx                 #   Main application
│   │   ├── components/             #   UI components
│   │   │   ├── FileUpload.jsx
│   │   │   ├── ProcessingOptions.jsx
│   │   │   └── ResultsDisplay.jsx
│   │   └── services/api.js         #   API client
│   └── package.json
│
├── backend/                        # FastAPI Backend
│   └── main.py                     #   REST API endpoints
│
├── data/
│   └── arabic_fixes.json           # 186 Arabic corrections
├── data_seed/
│   └── correction_dict_seed.json   # Seed data
├── artifacts/
│   └── correction_dict.json        # Learned corrections
│
├── src/                            # HandwrittenOCR Engine
├── mobile/                         # Static PWA (offline review)
├── training/                       # HTR Training System
│   ├── README.md                  #   Training overview
│   ├── configs/                   #   Training configurations
│   │   └── trocr_lora_arabic.yaml #     TrOCR + LoRA settings
│   ├── scripts/                   #   Training scripts
│   │   ├── prepare_htr_dataset.py #     Dataset preparation
│   │   ├── train_trocr_lora.py    #     LoRA training
│   │   ├── evaluate_checkpoint.py #     Model evaluation
│   │   ├── active_learning_pipeline.py  # Active learning
│   │   └── generate_synthetic_data.py   # Synthetic data generation
│   ├── models/                    #   Training models
│   │   └── lora_htr_trainer.py    #     Custom LoRA trainer
│   └── data/                      #   Data connectors
│       └── mobile_review_connector.py  # Mobile review sync
├── Dockerfile.training             # Training Docker image
├── requirements-training.txt       # Training dependencies
├── Makefile                        # Build & training commands
├── justfile                        # Alternative build commands
│
├── mobile_review/                  # Flask server (remote team review)
│   ├── server.py                  #   REST API review server
│   ├── templates/review.html      #   Touch-friendly review UI
│   └── README.md                  #   mobile/ vs mobile_review/ guide
├── tests/                          # pytest test suite (13+ files)
├── .github/workflows/              # CI/CD
│   ├── ci.yml                     #   Tests on push/PR
│   └── release.yml                #   Auto-release on tags
├── notebooks/                      # Jupyter Notebooks
│   ├── OmniFile_Gradio_Debugger.ipynb  #   Gradio interactive debugger (Colab-ready)
├── docs/                           # Documentation
│   ├── TESTING_GUIDE.md          #   Testing & development guide
│   ├── API_DOCS.md
│   ├── USER_GUIDE.md
│   └── DEVELOPER_GUIDE.md
├── k8s/                            # Kubernetes manifests
│   ├── namespace.yaml
│   ├── backend.yaml
│   ├── celery.yaml
│   ├── redis.yaml
│   ├── nginx.yaml
│   ├── hpa.yaml
│   └── storage.yaml
└── examples/                       # Usage examples
    ├── ocr_basic.py
    ├── nlp_pipeline.py
    └── evaluation_example.py

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🧩 Module Descriptions | وصف الوحدات

1. 🎯 modules/core/ — Core Data Models

The foundational layer defining all shared data structures using Pydantic v2. Provides type-safe models for OCR results, processing options, document metadata, and inter-module communication.

File	Description
structure.py	Pydantic v2 models for OCRResult, ProcessingOptions, Document, BoundingBox, and all shared types

---

2. 👁️ modules/vision/ — Computer Vision & OCR Engine

The heart of the system. Handles image preprocessing, OCR with 4 engines, result fusion, PDF processing, layout analysis, table extraction, and text reconstruction.

File	Description
ocr_engine.py	Multi-engine OCR (TrOCR, EasyOCR, Tesseract, PaddleOCR) with ONNX Runtime & INT8 quantization
image_preprocessor.py	CLAHE, Gaussian denoise, deskew (Hough), Otsu thresholding, adaptive binarization
pdf_processor.py	PyMuPDF-based PDF processing with pdf2image fallback
text_reconstructor.py	RTL/LTR sentence reconstruction with language-aware ordering
result_fusion.py	4 fusion strategies: highest confidence, weighted average, voting, longest text
layout_analyzer.py	Document layout analysis — tables, headers, footers, sections
table_extractor.py	Table extraction using Hough lines + contour analysis

---

3. 🗣️ modules/nlp/ — Natural Language Processing

Multilingual text processing including spell correction, translation, summarization, entity extraction, text classification, and Arabic RTL handling.

File	Description
spell_corrector.py	3-language spell correction (AR, EN, DE) with user learning & Python keyword protection
translator.py	Helsinki-NLP/opus-mt machine translation (6 language pairs)
summarizer.py	BART summarization (English + Arabic)
entity_extractor.py	BERT-based Named Entity Recognition
language_detector.py	Automatic language detection (AR, EN, DE)
text_classifier.py	6-category text classification
arabic_rtl.py	Full RTL processing — arabic_reshaper + python-bidi + 40+ normalization rules
mixed_text.py	Arabic/English/number mixed text handler with medical term protection
ai_corrector.py	GPT-based OCR correction with context awareness
correction_dict.json	186+ common Arabic OCR error corrections

---

4. 🔒 modules/security/ — Security & Privacy

Comprehensive security module for PII detection, encryption, code protection, file organization, backup management, and audit logging.

File	Description
file_scanner.py	File security scanning and validation
sensitive_data_scanner.py	PII detection using Microsoft Presidio + detect-secrets
encryption.py	Fernet symmetric encryption (AES-128) with folder support
code_protector.py	Prevents spell correction inside code blocks (Python, JS, etc.)
file_organizer.py	Automatic file organization by type and content
archive_handler.py	ZIP archive creation/extraction with integrity checks
backup_manager.py	Automatic and manual backup management
audit_logger.py	File + Redis audit trail with statistics
secure_file_handler.py	Path traversal prevention + safe tempfile handling

---

5. 🤖 modules/ai/ — AI Enhancement

Advanced AI capabilities including self-learning pattern matching, Gemini-based refinement, and a universal AI model gateway.

File	Description
pattern_matcher.py	SSIM-based visual pattern matching — learns from corrected word images
pattern_db.py	SQLite database for storing and retrieving visual OCR patterns
gemini_refiner.py	Google Gemini API integration for context-aware OCR refinement
gateway/	OmniFile AI Gateway — Universal proxy for routing AI model requests to multiple providers (see below)

🌐 modules/ai/gateway/ — OmniFile AI Gateway

A universal AI model proxy that intercepts Messages API requests and routes them to alternative providers. Supports 8 provider backends with automatic format conversion and SSE streaming.

Subdirectory	Description
api/	FastAPI application layer — routes, auth, model routing, request optimization
api/models/	Pydantic request/response models for the Messages API protocol
api/web_tools/	Local web search and fetch handling with SSRF protection
config/	Central settings, provider catalog, logging configuration
core/	Protocol helpers — SSE formatting, message conversion, token estimation
core/anthropic/	Native Messages API support — content blocks, thinking tags, tool parsing
providers/	Provider transport implementations — OpenAI-compatible and native protocols
providers/deepseek/	DeepSeek provider (native Messages API)
providers/nvidia_nim/	NVIDIA NIM provider (OpenAI-compatible)
providers/open_router/	OpenRouter provider (native Messages API)
providers/ollama/	Ollama local provider (native Messages API)
providers/lmstudio/	LM Studio local provider (native Messages API)
providers/llamacpp/	llama.cpp local provider (native Messages API)
providers/kimi/	Kimi/Moonshot provider (OpenAI-compatible)
providers/wafer/	Wafer provider (native Messages API)
server.py	ASGI entry point for the gateway proxy
pool/	Advanced Pool Management — Account pooling, rate limit fallback, conversation reuse, health scoring

🔄 modules/ai/gateway/pool/ — Advanced Pool Management

Intelligent multi-account management system with automatic failover, rate limit distribution, and conversation context reuse.

File	Description
account_pool.py	AccountPool — Multi-account rotation with priority ordering, LRU selection, concurrency control, and automatic failover after consecutive failures
rate_limit_fallback.py	RateLimitFallbackManager — Smart model fallback when rate-limited: auto-detects effort tiers (low/medium/high/xhigh/max), picks same-provider alternatives first, then cross-provider
conversation_pool.py	ConversationPool — Multi-turn conversation reuse via stable fingerprinting: normalizes dynamic tokens (dates, UUIDs, CWDs), TTL-based LRU eviction (500 entries, 30 min default)
health_scorer.py	ProviderHealthScorer — Weighted health scoring (0.0-1.0) based on success rate, latency percentiles (p50/p95), error type tracking, and consecutive failure streaks

---

6. 📤 modules/export/ — Multi-Format Export

Export processed documents to 6 different formats with proper RTL support.

File	Description
exporter.py	Export to DOCX (RTL bidi), HTML (dir="rtl"), searchable PDF (invisible text), Excel (RTL alignment), JSON (full structure with BBox), TXT (UTF-8 BOM)

---

7. 📊 modules/evaluation/ — Evaluation & Metrics

OCR accuracy evaluation with Arabic-aware normalization.

File	Description
metrics.py	CER/WER computation, Arabic text normalization, Levenshtein distance (zero dependencies), quality grading (A+ to F)

---

🔗 API Documentation | توثيق API

The FastAPI backend exposes the following REST endpoints. Full interactive documentation is available at /docs (Swagger UI) when the backend is running.

Base URL

http://localhost:5001/api/v1

Endpoints

Method	Endpoint	Description
POST	/ocr/process	Process an image/PDF file with OCR
POST	/ocr/process-batch	Batch process multiple files
GET	/ocr/result/{task_id}	Get OCR result by task ID
POST	/nlp/correct	Spell-correct text (AR/EN/DE)
POST	/nlp/translate	Translate text between languages
POST	/nlp/summarize	Summarize text
POST	/nlp/entities	Extract named entities
POST	/nlp/classify	Classify text into categories
POST	/export/{format}	Export results to DOCX/HTML/PDF/JSON/TXT/Excel
POST	/security/scan	Scan file for PII and security issues
POST	/security/encrypt	Encrypt a file
POST	/security/decrypt	Decrypt a file
GET	/health	Health check endpoint
GET	/metrics	System performance metrics

📖 For the complete API reference with request/response schemas, see docs/API_DOCS.md or access the Swagger UI at /docs.

---

📸 Screenshots | لقطات الشاشة

Streamlit UI	Gradio UI	React Frontend
6-tab interface	7-tab interface	Dark/Light mode

OCR Processing	Arabic RTL	Table Extraction
Multi-engine OCR	Full RTL support	Structured data

📝 Screenshots will be added in a future update. For now, try the live demo to see the application in action.

📋 See CHANGELOG.md for the complete version history.

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📊 Project Statistics | إحصائيات المشروع

Metric	Value
Python Files	155+
Lines of Code	~40,000+
Total Files	230+
OCR Engines	4 (TrOCR, EasyOCR, Tesseract, PaddleOCR)
Fusion Strategies	4
Supported Languages	3 (EN, AR, DE)
Export Formats	6 (DOCX, HTML, PDF, JSON, TXT, Excel)
Test Files	13
Merged Projects	6
Security Modules	9
NLP Capabilities	10
AI Gateway Providers	8 (DeepSeek, NIM, OpenRouter, Ollama, LM Studio, llama.cpp, Kimi, Wafer)
API Endpoints	14+

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🌍 Supported Languages | اللغات المدعومة

Language	Code	RTL Support	OCR	Spell Check	Translation
🇸🇦 العربية (Arabic)	ar	✅	✅	✅	✅
🇬🇧 English	en	❌	✅	✅	✅
🇩🇪 Deutsch (German)	de	❌	✅	✅	✅

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🤝 Contributing | المساهمة

Contributions are welcome! Please follow these steps:

كيف تساهم / How to Contribute

1. Fork the repository
2. Clone your fork locally

   git clone https://github.com/your-username/OmniFile_Processor.git
   cd OmniFile_Processor

3. Create a feature branch

   git checkout -b feature/your-feature-name

4. Make your changes and ensure tests pass

   pip install -r requirements-full.txt          # Everything (~6-8 GB, ~15-30 min)
   

Or install in layers:

pip install -r requirements-core.txt # Minimum (~1.5 GB, ~3 min)

pip install -r requirements-core.txt -r requirements-ocr.txt # + OCR engines

pip install -r requirements-core.txt -r requirements-nlp.txt # + NLP

pytest tests/ -v

5. **Commit** with a descriptive message
```bash
git commit -m "feat: add your feature description"

6. Push to your fork

   git push origin feature/your-feature-name

7. Open a Pull Request against the main branch

Development Guidelines

• Follow PEP 8 style guidelines
• Add docstrings to all new functions and classes
• Write tests for new features (place in tests/)
• Update the relevant documentation in docs/
• Use type hints throughout your code
• Ensure RTL handling is tested for any text-related changes

---

📜 License | الترخيص

This project is licensed under the MIT License.

MIT License

Copyright (c) 2026 Dr Abdulmalek Tamer Al-husseini — Homs, Syria

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

See LICENSE for the full text.

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🔗 Links | الروابط

Resource	Link
🐙 GitHub Repository	https://github.com/DrAbdulmalek/OmniFile_Processor
🤗 HuggingFace Spaces	https://huggingface.co/spaces/DrAbdulmalek/handwriting-ocr
📚 User Guide	docs/USER_GUIDE.md
👨‍💻 Developer Guide	docs/DEVELOPER_GUIDE.md
🧪 Testing Guide	docs/TESTING_GUIDE.md
📡 API Documentation	docs/API_DOCS.md
💡 Suggestions	SUGGESTIONS.md
📋 License	LICENSE

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Built with ❤️ by Dr Abdulmalek Tamer Al-husseini 📍 Homs, Syria  |  📧 Abdulmalek.husseini@gmail.com

⭐ If you find this project useful, please give it a star on GitHub!

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Repository Status

Field	Value
Role	Legacy AI File Processing System
Status	Legacy — Migration Source
Layer	Legacy / Archive
Priority	Low
Migration Target	omni-medical-suite

Migration Notice

OmniFile_Processor v5.0 has been merged into omni-medical-suite v2.0. This repository is preserved as a reference and migration source.

Migration Path

Component	Source (This Repo)	Destination
Multi-Engine OCR	OmniFile_Processor	omni-medical-suite (packages/omni-ocr)
NLP Pipeline	OmniFile_Processor	omni-medical-suite (packages/nlp)
AI Gateway	OmniFile_Processor	omni-medical-suite (packages/ai)
Export System	OmniFile_Processor	omni-medical-suite (packages/export)
Streamlit UI	OmniFile_Processor	omni-medical-suite (apps/)
Gradio UI	OmniFile_Processor	omni-medical-suite (services/)
React UI	OmniFile_Processor	omni-medical-suite (apps/web)

Who Should Use This

• Developers referencing legacy implementations during migration
• Teams needing to extract specific modules not yet migrated
• Users of the HuggingFace Spaces deployments (still active)

When to Use This vs omni-medical-suite

Need	Repository
New development / unified platform	omni-medical-suite
Reference legacy implementations	This repo (OmniFile_Processor)
Active HuggingFace demo	HF Space

Related Repositories

Repo	Role	Status
omni-medical-suite	Main Platform (Successor)	Active
medical-ocr-postprocessor	Core Correction Engine	Active
medical-handwriting-ocr	Production OCR	Active

License: MIT — Dr. Abdulmalek Tamer Al-husseini