VisionDoc_AI: A Local LLM-Based Intelligent Document Processing Platform

VisionDoc_AI is an open-source intelligent document processing solution maintained by vimaladityaraj, hosted on GitHub (link: https://github.com/vimaladityaraj/VisionDoc_AI) and updated on 2026-06-16T21:44:51Z. It combines OCR, local large language models (LLM), and modern web technologies to automate information extraction and structuring for invoices, receipts, forms, and other documents. It emphasizes local deployment to ensure data privacy, addressing limitations of traditional OCR and cloud-based solutions. Key features include multi-format document support, multi-engine OCR integration, LLM-powered classification/extraction, and easy deployment via Docker Compose.

Traditional OCR solutions often struggle with complex document understanding, while cloud-based document processing platforms raise privacy concerns for sensitive data. VisionDoc_AI addresses these issues by adopting a local-first architecture, enabling document processing (OCR and LLM inference) to be completed on local servers without relying on third-party cloud APIs. This design ensures sensitive documents never leave the local environment.

Core Function Modules:

• Document Parsing: Supports PDF (scanned/native), images (JPEG/PNG/TIFF), Office docs, and multi-page docs with context association.
• OCR Integration: Multi-engine strategy (Tesseract, PaddleOCR, EasyOCR, optional Azure/AWS) for optimal results.
• Intelligent Classification: Local LLM-based categorization (invoices, receipts, forms, contracts, general docs).
• Info Extraction: Predefined templates for different document types (e.g., invoice fields: code, number, amount, date).

Technical Stack:

• Backend: FastAPI (async processing, Celery+Redis task queue, streaming responses).
• Frontend: Streamlit (drag-and-drop upload, real-time preview, result export).
• Local LLM: Ollama (model management, optimized prompts, structured JSON output).
• Storage: PostgreSQL (metadata), MinIO/S3 (files), Redis (cache/queue).

Local Deployment:

1. Clone repo: git clone https://github.com/vimaladityaraj/VisionDoc_AI.git
2. Navigate to directory: cd VisionDoc_AI
3. Start services: docker-compose up -d

Dependencies: Ollama, PostgreSQL, Redis, optional MinIO.

Usage Flow:

1. Upload docs via web interface.
2. Auto document type recognition.
3. OCR text extraction.
4. LLM semantic understanding and info extraction.
5. View/export structured results (JSON/CSV/Excel).

Local-First Design:

• All document processing (OCR, LLM) is done locally; no data upload to third-party services.

Enterprise Security:

• Role-based access control.
• Full audit logs for operations.
• Data encryption (transit/storage).
• Automatic sensitive info desensitization.

Application Scenarios:

• Finance: Batch invoice/receipt processing, ERP integration, auto bookkeeping.
• HR: Resume info extraction, onboarding form data entry, contract clause retrieval.
• Legal: Contract key info extraction, risk clause annotation, renewal reminders.
• Healthcare: Medical record structuring, report summarization.

Performance:

• Typical config (8-core CPU +16GB RAM +7B local model):
  • Single-page scan:3-5s;10-page doc:20-30s;100 invoices:10-15min.
• Accuracy: Document classification (>95%), key field extraction F1 (>90%), structured JSON validity (>98%).

vs Commercial Solutions:

Dimension	VisionDoc_AI	Cloud OCR	Traditional OCR
Cost	Low (open-source)	Pay-as-you-go	High (licensing)
Privacy	Fully local	Cloud upload	Local
Understanding	Strong (LLM)	Medium	Weak (template-based)
Flexibility	High	Low	Medium

Community:

• MIT license; contributions welcome (new templates, OCR optimizations, prompt engineering).
• Integration cases: ERP (SAP/Oracle), RPA, Kubernetes deployment.

Future Directions: Multi-modal support (charts/seals), handwriting recognition, document comparison, intelligent Q&A, mobile apps.

VisionDoc_AI merges LLM's semantic understanding with OCR, offering a privacy-preserving alternative to cloud-based document processing. It's ideal for enterprises handling sensitive data (finance, legal, healthcare) that need local deployment. The modular design allows easy customization for specific needs. We recommend teams evaluate it for their document automation workflows, especially if data privacy is a top priority.