Automating invoice data extraction with OCR for enhanced accuracy and efficiency.

Skillsets used: Optical Character Recognition (OCR), PaddleOCR, Non-Maximum Suppression (NMS), Data Processing, Excel Automation

🔍 What I did

• Developed an OCR-powered system to automate invoice data retrieval, reducing manual effort for businesses.
• Leveraged PaddleOCR to achieve 95-100% text recognition accuracy, ensuring precise data extraction.
• Implemented table extraction techniques, consolidating structured invoice data into Excel for seamless analysis.
• Applied non-maximum suppression (NMS) to remove redundant horizontal & vertical lines, optimizing data accuracy.
• Categorized extracted data based on balance values, enabling faster decision-making and financial analysis.

📈 Impact & Insights

• Eliminated manual data entry, significantly reducing processing time & human errors.
• Enhanced accuracy in financial data extraction, improving business workflow efficiency.
• Structured invoice data storage, making it easier for businesses to track, analyze, and optimize expenses.
• Streamlined decision-making, allowing businesses to make data-driven financial insights.

🚀 Learning Outcomes

• Gained expertise in OCR, text recognition, and table extraction techniques.
• Optimized data processing workflows for real-world business applications.
• Strengthened skills in automating financial document handling & structured data extraction.
• Explored the intersection of AI and business process automation.