LayoutLM / LayoutAI
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
Deterministic Python-based data extraction from PDF and image invoices using template matching.
Invoice2data is a high-performance Python library and CLI tool designed for the automated extraction of structured data from semi-structured PDF and image files. In the 2026 market, it stands as the gold standard for deterministic, cost-effective document processing where high accuracy is required without the latency or cost of Large Language Models. Its technical architecture relies on a modular template system (YAML/JSON) that uses regular expressions and structural anchors to pinpoint data fields like invoice numbers, VAT details, and line items. It supports a variety of OCR backends, including Tesseract, GOCR, and commercial APIs like Google Cloud Vision or Amazon Textract, allowing architects to balance cost and precision. The tool is particularly favored for its ability to handle 'known' invoice formats with 100% accuracy while providing a framework for community-driven template sharing. It is ideal for high-volume batch processing and integrates seamlessly into enterprise ERP pipelines via JSON/CSV exports or custom Python hooks. Its 2026 positioning emphasizes its role as a local-first, privacy-conscious alternative to SaaS-only extraction platforms, fitting perfectly into edge computing and secure on-premise workflows.
Supports Tesseract, GOCR, OCR.space, Google Vision, and AWS Textract backends via a modular plugin architecture.
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
The open-source toolkit for deep learning-based document image analysis and structured data extraction.
Automate contract review and revenue recognition with Generative AI-driven document intelligence.
Enterprise-Grade Document Intelligence and RAG-Driven Knowledge Synthesis.
Verified feedback from the global deployment network.
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Uses human-readable YAML files to define document structural anchors and regex field locations.
Unlike probabilistic AI models, it uses fixed rules to ensure zero hallucination for known document types.
Scans document content to identify the issuer and automatically selects the corresponding template from a library.
Provides hooks to run Python functions on extracted data (e.g., currency conversion, date formatting) before output.
Built-in support for multiple export formats including CSV, JSON, XML, and direct database injection.
Enables 100% on-premise processing with local OCR (Tesseract), ensuring sensitive financial data never leaves the network.
Manual entry of 5,000+ monthly invoices into accounting software.
Registry Updated:2/7/2026
Scanning 10 years of paper invoices for specific tax identification numbers.
Tracking fluctuating energy costs across 200 real estate properties.