Input (PDF Raw Stream)
%PDF-1.4 %âãÏÓ 1 0 obj <> endobj 2 0 obj <> endobj 3 0 obj <
Output (ParseGrid JSON)
{ "document_type": "invoice", "confidence_score": 0.998, "entities": { "invoice_number": "4091", "total_amount": { "value": 1240.50, "currency": "USD" } }, "tables": [], "processing_time_ms": 42 }
Architecture Deep-Dive

A multi-stage neural pipeline for industrial data extraction.

Our engine doesn't just read text; it reconstructs the semantic intent of the original layout.
01 / INGESTION

Lossless Normalization

Every document is converted into a high-fidelity tensor representation. We handle corrupt PDF streams, skewed mobile photos, and legacy scans with equal precision.

02 / VISION-OCR

Context-Aware Recognition

Unlike standard OCR, our vision models use spatial context to resolve ambiguous characters. We achieve 99.9% accuracy on financial figures and technical codes.

03 / TABLE DETECTION

Geometric Reconstruction

We identify row spans and column relationships without relying on visible grid lines. Complex nested tables are decomposed into clean, relational objects.

04 / REASONING

Entity Alignment

The final stage maps extracted data to your specific schema. Confidence scores are calculated at the field level, enabling automated high-trust workflows.

Deep Extraction Capabilities

Architected for precision in the most demanding document environments.

01. Table Parsing

Our neural-grid engine reconstructs complex hierarchical tables, handling merged cells, nested headers, and borderless layouts with mathematical rigour.

Logic Engine Grid-ResNet-v4
Output Format JSON / Parquet
Accuracy Benchmark ICDAR-2023
99.4 % F1 Score

02. Advanced OCR

Multi-language optical character recognition that excels in low-contrast, rotated, or degraded scans. High-fidelity spatial awareness preserves reading order.

Max Resolution 1200 DPI Sync
Languages 140+ Supported
Accuracy Benchmark Handwritten/Mixed
98.2 % CER (Raw)
Industry Benchmarks

Performance is not an abstraction. We measure ParseGrid against industry standards to ensure absolute precision.

Table Extraction F1-Score 01
98.4% Accuracy on complex financial nested tables.
Average Latency (ms) 02
42ms Median processing time for single-page PDFs.

Methodology

All benchmarks were conducted using the PubTabNet dataset and internal proprietary sets of scanned invoices and medical records. Hardware parity was maintained across all engine tests using dedicated compute instances to ensure zero-bias environment profiles.