Problem Statement:
A Next-gen technology firm’s OCR system suffered from slow processing, low accuracy, and incomplete data extraction, impacting efficiency.
Challenges:
- Poor Image Quality: Low contrast and noise reduced OCR accuracy.
- Inefficient Model Inference: Struggled with fonts, distortions, and orientations.
- High Latency: Slow processing hindered real-time data retrieval.
- Data Loss: Partial text recognition led to missing critical information.
Technology Stack:
- Python, OpenCV, MySQL, Postman, Jira
- Deep Neural Network (DNN) for OCR Optimization
- FastAPI for Asynchronous API Processing
- Dockerized Microservices for Scalability
Solution & Implementation:
- Advanced Image Pre-processing: Noise reduction, CLAHE contrast enhancement, and edge detection.
- DNN-based OCR: CRNN model with CNN, LSTM, and CTC loss for improved text recognition.
- Performance Optimization: GPU-accelerated processing, async API calls, and indexed MySQL queries.
- Agile Deployment: Managed in Jira, using Dockerized microservices for scalability.
Comparison: Old vs. New System
| Feature | Old System | New System |
| OCR Accuracy | 65% | 90%+ (DNN-powered OCR) |
| Processing Speed | Slow, Single-threaded | 2x Faster (Parallelized GPU Processing) |
| Response Time | 5-7 sec | 2-3 sec (Async API + Batch Processing) |
| Data Extraction | Partial (~50% loss) | 50% improvement |
| Scalability | Limited | Optimized for large datasets |
Results:
- 50% Increase in Data Extraction Accuracy
- 40% Reduction in Processing Time
- Seamless Real-time & Batch Processing
Conclusion:
By leveraging deep learning, parallel computing, and database optimizations, the technology enterprise enhanced OCR accuracy, reduced latency, and improved scalability, leading to a robust, high-performance data extraction system.
