Smart OCR System for Structured Data Extraction (Invoices, IDs, Forms)

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