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.