Intelligent Document Processing leveraging Computer Vision

Reads text, understands the document’s layout, structure, and context. Automatically identifies document type –Invoice, Receipt, Contract, ID, Form) without prior setup for each new type. Analyzes spatial relationships between text blocks, tables, and visual elements. 

Extracts specific data fields (e.g., invoice number, date, total amount, vendor name, line items) based on AI understanding, not rigid pixel coordinates or templates. Adapts to variations in document layouts. Identifies labels (like “Total Amount:”) and their corresponding values. Accurately identifies and extracts data from tables, including complex or multi-page tables. 

Also known as Intelligent Character Recognition (ICR). Improves the ability to read handwritten text, signatures, or checkboxes compared to traditional methods, leveraging AI trained on diverse handwriting styles.

Automatically validates extracted data against predefined rules, databases, or internal business logic (e.g., checking if an invoice number format is correct, verifying vendor details against a master list).

Automatically pushes extracted, validated data into other business systems (ERP, CRM, accounting software, databases, RPA bots) via APIs or connectors. Routes documents or extracted data based on content (e.g., route invoices over a certain amount for approval).

Handles a wide variety of document types and layouts without requiring custom templates for each variation. Includes AI-enhanced features for auto-orientation, deskewing, noise reduction, and contrast adjustment to improve recognition accuracy even on poor-quality scans. 

Capably processes large batches of documents automatically and efficiently, scaling to meet enterprise demands without manual intervention per document. 

AI models learn from corrections or new document captures and types encountered, improving accuracy over time with minimal retraining. Includes an interface for human review of low-confidence extractions, where the human input helps refine the AI model. 

AI models support recognition and understanding across a wider range of languages and character sets.