SystemsCompleted Pipeline2024

Intelligent Document Automation System

Role: Software & Automation Engineer

High-throughput document processing pipeline extracting structured data from unstructured enterprise receipts, invoices, and certificates.

Verified Project Links & Repositories

2 Active Resources

GitHub Repository (Main)

Primary verified codebase, implementation, and commit history

Technical Documentation

Architecture diagrams, API references, and setup guide

Technology Stack & Libraries

PythonOpenCVTesseract OCRFastAPIPandasJSON Schema

The Challenge / Problem

Context & Objectives

Manual data entry from physical forms and scanned invoices creates administrative bottlenecks, high error rates, and compliance risks.

Architectural Solution

Engineered Approach

Developed an automated ingestion pipeline that preprocesses document skew, extracts tabular fields, and validates mathematical sums before database entry.

System Architecture & Pipeline

Document Queue -> OpenCV Dewarping -> Tesseract / PaddleOCR -> Layout Parser -> Schema Validator -> Export Dispatcher.

Built an enterprise-grade document automation system that converts chaotic scanned PDF files into structured JSON schemas. Combines layout-aware OCR with rule-based regex and heuristic validators to ensure zero data omission in enterprise record keeping.

Engineered Capabilities & Innovations

Automatic perspective correction, deskewing, and noise removal on mobile camera uploads

Table recognition and key-value pair parsing with high boundary accuracy

Mathematical reconciliation checks (line items sum to invoice subtotal)

REST API endpoints for batch document ingestion and webhook notifications

Empirical Results & Benchmarks

Reduced invoice data entry turnaround time from 6 minutes per page to 1.8 seconds with a 97.4% field-level extraction accuracy.

Have Questions About This System?

I am always glad to discuss technical architecture, benchmarks, or potential collaboration.

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