Intelligent Document Processing (IDP)

At the core of IDP lies advanced Optical Character Recognition (OCR), which converts scanned documents, PDFs, and images into machine-readable text. Modern OCR engines can recognize printed and handwritten content, detect document structure, and enhance low-quality scans using image preprocessing techniques such as noise reduction and alignment correction. This ensures that even complex or imperfect documents can be reliably digitized before further processing.

NLP techniques enable deeper understanding of extracted text. Instead of simply reading characters, IDP systems identify entities such as names, dates, amounts, addresses, and contractual terms. Context-aware processing helps interpret meaning within paragraphs and detect relationships between data points, making it possible to work with semi-structured and unstructured documents effectively.

Machine learning models analyze document layout and content to classify document types and extract relevant fields. These models are trained to recognize patterns across different formats, allowing them to adapt to varying invoice templates, contract structures, or form designs. Over time, feedback loops and additional training data improve accuracy and reduce the need for manual corrections.

Once data is extracted, validation engines apply business rules to ensure consistency and accuracy. Totals can be recalculated, required fields verified, and references cross-checked against internal databases. After validation, structured data is automatically routed into enterprise systems such as ERP, CRM, or document management platforms through APIs and workflow engines, enabling end-to-end process automation.