RFC // BackendRole: Backend & Systems Engineer
ReceiptLens Engine
Resilient document ingestion and heuristic data extraction pipeline.
Production Benchmarks & Targets
Parsing Accuracy
97.8%
Multi-pass image normalization
Worker Concurrency
50 Tasks/Pod
Async image buffer pipeline
API Availability
99.95%
Circuit-breaker guarded workers
System Architecture Invariants
- •Pipelined image preprocessing (adaptive thresholding, deskewing) before OCR ingestion.
- •Structured JSON schema extraction enforced via Pydantic validators with graceful fallback parsing.
- •Decoupled extraction workers protected by exponential backoff and dead-letter queues (DLQ).
Security & Threat Mitigations
- •Zero disk persistence of raw unencrypted PII image files during parsing lifecycles.
- •MIME-type binary signature validation to prevent malicious payload uploads disguised as image files.
Problem & System Constraints
Unstructured receipt images have high variance in orientation, lighting, and layout, making naive OCR slow, error-prone, and memory-intensive.
Architectural Solution
Constructed an asynchronous extraction pipeline that normalizes raw image streams, applies parallel heuristic OCR parsing, and enforces structured relational schema outputs with strict field-level validation.