Overview
This case study documents the end-to-end engineering of a high-throughput OCR system, scaled to process 50,000+ complex, multi-lingual documents daily. The focus was strictly on achieving sub-100ms P99 latency while maintaining high precision on severely degraded scans.
Pipeline Architecture
A parallelized multi-stage process involving DBNet localization, affine rotation correction, and CRNN translation networks, heavily optimized via ONNX runtime optimizations.
Technical Execution
- Graph Optimization: Transitioned research-grade PyTorch models into production by exporting to ONNX and pruning redundant network operations, resulting in a 3.5x reduction in inference time.
- Serving Scale: Deployed the compiled graphs via NVIDIA Triton Inference Server, enabling dynamic batching and maximizing GPU utilization under heavy concurrent loads.