Overview

Our Document Extraction Service is an AI-driven OCR and document understanding engine that transforms complex, unstructured documents into structured, machine-readable data.

It forms the backbone of Beltic’s data extraction and automation layer, enabling intelligent ingestion and contextual parsing across financial, identity, and risk workflows.

Built on a hybrid stack of multimodal vision-language models (VLMs), high-precision OCR systems, and reasoning-enabled post-processing pipelines, the service achieves state-of-the-art performance on diverse global document types — including identity records, invoices, financial statements, and handwritten submissions.

Architecture Overview

Our Document Extraction Solution is composed of several cooperative subsystems, each handling a distinct stage of the extraction lifecycle.

1. Pre-Processing Layer

Prepares and normalizes documents before inference.

• Performs document cleaning and normalization, including adaptive denoising, contrast enhancement, and binarization.

• Executes layout and segmentation analysis to isolate text regions, tables, handwriting blocks, and graphical components.

• Applies feature extraction and embedding generation to encode visual and textual cues.

• Produces structured multimodal prompts for downstream VLM inference.

Impact:

Ensures consistent, high-quality inputs to the extraction models and optimizes performance across varied image qualities and document sources.

2. Processing Engine

The core inference layer dynamically selects and executes the optimal model pipeline for each document class.

• OCR Models:

  High-accuracy recognition systems trained on multilingual and multiscript datasets — supporting Latin, Arabic, Cyrillic, CJK, and complex right-to-left scripts.

• Vision-Language Models (VLMs):

  Multimodal architectures that integrate spatial layout, textual semantics, and visual reasoning to interpret structured and semi-structured documents.

• Non-Multimodal Models:

  Lightweight OCR engines optimized for batch operations, where contextual reasoning is unnecessary.

Impact:

Automatic pipeline selection balances accuracy and latency, enabling the system to process both low-complexity identity cards and multi-page financial statements efficiently.

3. Post-Processing and Validation

After inference, results pass through reasoning-based normalization and validation pipelines.

• Executes schema alignment, cross-field validation, and semantic normalization.

• Applies version-controlled post-processors with rollback and reproducibility guarantees.

• Tracks field-level accuracy, recall, and confidence intervals for continuous QA.

• Ensures all outputs conform to predefined or adaptive schemas.

Impact:

Transforms raw OCR output into clean, validated, and semantically structured datasets suitable for direct integration or downstream AI reasoning.

Continuous Learning and Adaptive Accuracy

The system improves autonomously through feedback-driven learning and performance benchmarking.

• Self-Supervised Feedback Loops: Leverage anonymized embeddings from prior documents to refine model generalization over time.

• Automated Drift Detection: Identifies model degradation or schema inconsistencies across data cohorts.

• Incremental Training: Adapts to new document languages, templates, and handwriting variants without full retraining cycles.

• Versioned Deployment: Supports safe A/B testing and side-by-side evaluation of model updates.

Impact:

Ensures accuracy scales with usage volume while maintaining traceable, versioned reliability for regulated environments.

Intelligent Schema Optimization

Schema intelligence ensures extraction output remains structurally and semantically correct across evolving document formats.

• Autonomous Schema Generation: Agents derive and refine extraction schemas from document structure and performance feedback.

• Field-Level Evaluation: The internal reasoning engine scores and adjusts precision/recall at each schema node.

• Dynamic Adaptation: Adjusts automatically for new layouts, templates, or unseen document variants.

Impact:

Minimizes manual schema maintenance and accelerates onboarding of new document types.

Multimodal Reasoning

Beltic’s extraction stack extends beyond OCR into contextual understanding.

• Combines visual layout cues (e.g., checkboxes, handwriting zones, table grids) with textual semantics for contextual reasoning.

• Recognizes document hierarchies—sections, headers, tables, annotations—and reconstructs them into machine-readable object trees.

• Outputs semantically organized, LLM-ready data optimized for summarization, classification, RAG, or compliance analytics.

Impact:

Enables document understanding comparable to human review while maintaining automation throughput.

User-Facing Document Flows

To make complex document extraction accessible across user profiles, the service provides configurable document flows.

• Define ingestion, classification, and extraction steps via API or dashboard.

• Upload or stream documents; select predefined or custom extraction profiles.

• Retrieve structured outputs (JSON, Markdown, CSV) through low-latency endpoints.

Impact:

Combines enterprise scalability with developer simplicity and low operational friction.

Output and Integration

Output Capabilities

• Structured JSON outputs with hierarchical layout, table reconstruction, and field-level confidence scores.

• Markdown representations for AI/LLM pipelines and Beltic Workflows.

• Streaming responses for real-time ingestion in event-driven architectures.

Evaluation and Monitoring

Each OCR/VLM version is continuously benchmarked for reliability and performance.

• Internal evaluation frameworks compute per-field precision, recall, and confidence distributions.

• Automated regression tests detect output drift or schema mismatch.

• Metrics and alerts integrate into Beltic’s observability and monitoring stack.

• Model and schema versions are fully traceable and auditable for compliance.

Impact:

Guarantees reproducible, transparent performance across deployments and regulatory audits.

Example Flow

1. Upload a document (e.g., ID, invoice, or financial statement).

2. Pre-processing normalizes, segments, and embeds the document.

3. The inference engine selects the optimal OCR or VLM pipeline.

4. Post-processing applies reasoning and schema validation.

5. The system returns a structured, confidence-scored output in seconds.