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Core Engine

Hyper-Localized Arabic OCR

Regional Perception Engine (RPE)

A specialized text-extraction layer built to handle the 'messy middle' of GCC bureaucracy. Designed to achieve 95–99% accuracy on real GCC administrative documents, even when quality is poor and layouts are inconsistent.

"Built for the documents global OCR fails on."

95-99% AccuracyNative ArabicSovereign-Safe

Why Hyper-Localized OCR Exists

Global Platforms Assume

  • ✓Clean PDFs
  • ✓Structured English text
  • ✓Western document formats

GCC Enterprises Deal With

  • 📄Handwritten customs forms
  • 📄Stamped and signed contracts
  • 📄Scanned ministry circulars
  • 📄Mixed RTL & LTR content
  • 📄Low-resolution photos & faxes

Generic OCR achieves <75% accuracy. Hyper-Localized OCR closes the 'Arabic gap' with regional data, script-aware models, and sovereign deployment.

The "Messy Middle" Challenge

Why standard OCR fails on bidirectional documents

Generic Global OCR
INV-2024[GARBLED]
?@#%Total: 500
Misaligned Direction
VeloProcess RPE
فاتورة ضريبيةINV-2024
الإجمالي500.00 SAR

OCR Processing Pipeline

From raw image to structured output

Input

Preprocess

Detect

Extract

Output

Capabilities

What Hyper-Localized OCR Does

The Regional Perception Engine performs specialized text extraction optimized for GCC documents.

High-accuracy Arabic OCR
Mixed Arabic/English extraction
Handwriting recognition (HWR)
Forensic image enhancement
Template-aware field extraction
Sovereign-safe inference

Output: Structured, machine-readable fields ready for downstream validation and automation.

Core Capabilities

Six Pillars of Arabic Intelligence

RPE combines multiple specialized technologies to deliver unmatched Arabic document processing.

Multi-Engine OCR

Tiered extraction pipeline with compliance-aware engine selection.

  • Tier 1: Hyperscale OCR (Google/Azure Vision)
  • Tier 2: Sovereign Local OCR (PaddleOCR/EasyOCR)
  • Compliance Toggle for sensitive documents

Native Arabic Script

Built specifically for Arabic's unique challenges.

  • Context-dependent letter shapes
  • Cursive connections & ligatures
  • Diacritics handling
  • Regional typography variations

Mixed-Direction (RTL + LTR)

Correctly handles documents with both Arabic and English.

  • Invoice numbers & serial codes
  • Vehicle IDs & passport references
  • Single structured output

Forensic Enhancement

Automatic image enhancement before OCR processing.

  • De-noising & de-skewing
  • Contrast normalization
  • Resolution correction
  • Optimized for faxes & photos

Handwriting Recognition

Specialized models for handwritten content.

  • Handwritten notes & annotations
  • Signatures recognition
  • Margin comments extraction

Template-Aware Extraction

Recognizes regional document layouts for improved accuracy.

  • ZATCA invoices & Iqamas
  • Saudi National IDs & Nitaqat reports
  • Customs & logistics forms
Pipeline Integration

How Hyper-Localized OCR Fits Into VeloProcess

RPE is the first perception layer in the VeloProcess pipeline. If perception fails, everything downstream fails.

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OCR Process

Verify

Workflow

1

Document Ingested

2

Image Enhancement & OCR

3

Field Extraction

4

DAV Verification

5

CaC Validation

6

Ledger Notarization

7

Workflow Execution

RPE is mission-critical — it enables DAV, Compliance-as-Code, and the Sovereign Ledger to function reliably.

Applications

Industry Use Cases

Hyper-Localized OCR powers automation across GCC industries.

Government

  • Intake forms
  • Ministry circulars
  • Permits & certificates

Logistics

  • Airway bills
  • Packing lists
  • Handwritten manifests

Finance

  • Invoices
  • Receipts
  • Tax documents

HR

  • Contracts
  • IDs
  • Employee forms
Sovereignty & Compliance

Built for Regulated Environments

Hyper-Localized OCR is designed for data residency and compliance requirements.

Security Features

  • Local inference for KSA & UAE tenants
  • No PII leaves jurisdictional borders
  • Air-gapped deployment for Class-C data
  • Encrypted intermediate artifacts
  • Automatic PII redaction (optional)

Local Cloud

AWS Bahrain / Azure UAE

On-Premises

Private Data Centers

Air-Gapped

Class-C Environments

All document processing stays within sovereign boundaries

Compliance Standards

KSA PDPLUAE Data ResidencyNCA ECC

Performance Targets

Industry-leading accuracy on Arabic and mixed-language documents.

0%

Accuracy

0ms

Speed (ms)

0+

Languages

0+

Formats

≥95%

CCR on mixed documents

99%

On regional templates

>20%

Better than generic OCR

50MB+

Scan handling capacity

Key Benefits

Transform Arabic document processing with sovereign-grade OCR.

Unlocks Arabic automation
Reduces correction costs
Enables compliance
Works where others fail
Sovereign by design

See Hyper-Localized OCR in Action

Experience the Regional Perception Engine processing your actual GCC documents with unmatched accuracy.

Request a Demo