We are building a high-accuracy OCR system that extracts structured data from scanned documents and images such as invoices, receipts, ID cards, and application forms. This is not just text recognition — the goal is to build a reliable pipeline that converts messy, real-world documents into clean, validated, structured JSON data ready for databases or ERP systems. -Objective Develop an intelligent OCR engine that: Extracts text from scanned PDFs and images (JPG, PNG) Detects document type automatically (invoice, ID card, form, etc.) Identifies key-value pairs (e.g., Name, Date, Total Amount, Invoice No) Handles noisy images (low resolution, skewed, shadows) Returns structured JSON output Achieves high accuracy (≥95% on test dataset) -Technical Scope - Image Preprocessing Deskewing Noise reduction Contrast enhancement Orientation detection - OCR Engine Tesseract / EasyOCR / PaddleOCR (or custom-trained model) Multi-language support (English required, others optional) - Intelligent Field Extraction Regex + NLP-based entity detection Layout-aware parsing Table detection for invoice line items -Validation Layer Date format validation Currency normalization Email/Phone validation Confidence scoring per field -Deliverables Complete source code REST API endpoint Sample dataset testing results Accuracy report Deployment guide - Bonus (Optional but Preferred) Training custom OCR model Table structure recognition Handwriting recognition Cloud deployment (AWS/GCP/Azure) - Budget Open to proposals based on experience and solution quality. - Ideal Freelancer Strong computer vision background Experience with OCR pipelines Experience handling real-world noisy documents Can explain technical decisions clearly