Dynamic Schema Discovery & Computer Vision Ingestion Engine

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System Architecture

This implementation focuses on an end-to-end pipeline that bridges the gap between unstructured visual inputs and a rigid processing engine. The core innovation is the decoupling of the data requirements from the UI, achieved through automated static analysis of the calculation modules.

Technical Implementation

Static Analysis via AST

To ensure the ingestion layer is always synced with the logic engine, I implemented a schema discovery routine using Python’s ast module.

Benefit: This allows the UI to dynamically provision itself based on the underlying logic's requirements, eliminating the need for manual field mapping and reducing technical debt during logic iterations.

Computer Vision Pipeline

The ingestion layer utilizes OpenCV to perform spatial feature extraction from unstructured document payloads (PDF/Images) via hierarchical contour filtering and aspect-ratio constraints.

Benefit: This allows the system to programmatically isolate data cells within a visual field, transforming a raw image into a structured grid of coordinate-aware "data pods" without hardcoding pixel locations.

Image Optimization Suite

Captured regions undergo Lanczos4 interpolation for high-fidelity upscaling and Otsu’s binarization to normalize the visual input before it reaches the OCR engine.

Benefit: This significantly increases the signal-to-noise ratio in low-fidelity or "noisy" captures, ensuring that the downstream OCR maintains high accuracy even when processing degraded source documents.

Regex Normalization & Sanitization

I implemented a robust regex-based cleaning layer that intercepts varied OCR string outputs and transforms them into standardized floating-point values.

Benefit: This prevents "Engine Bust" errors by ensuring the core calculation logic only receives sanitized, type-validated numbers, effectively creating a computational buffer.

Contextual Harvesting Logic

The system employs "Contextual Anchors" to map identified visual regions to the discovered schema, using size-thresholding to filter out non-target artifacts.

Benefit: This ensures that the engine only ingests relevant data, automatically discarding noise and visual artifacts that would otherwise corrupt the telemetry manifest.

Operational Interface (Mission Control)

The UI serves as a real-time verification stack, providing immediate visual feedback upon payload verification and data integrity checks.

Benefit: This enables "Human-in-the-Loop" validation, allowing an operator to oversee and override the automated ingestion process, which is critical for maintaining 100% accuracy in high-stakes environments.

Dependency Synchronization

I included a pre-initialization routine to verify $PATH parity for critical system-level dependencies like Tesseract and Poppler.

Benefit: This check ensures that the entire ground stack is synchronized and operational before the system attempts a mission, preventing runtime failures due to missing host-system components.