Blog Articles - ai

Summary: Handwriting recognition software helps organizations extract data from handwritten documents such as medical forms, insurance claims, checks, shipment records, and government archives. This comparison examines five approaches: Google Document AI, AWS Textract, Azure Document Intelligence, Tesseract OCR, and Apryse Intelligent Character Recognition (ICR). While cloud-based handwriting recognition APIs offer convenience, they often require documents to leave secure environments and charge per page. Open-source OCR tools have limited handwriting support and require a significant developer lift. Apryse ICR provides a dedicated handwriting recognition engine that runs on-premises, in private clouds, or air-gapped environments, enabling secure, scalable extraction of handwritten text into searchable PDFs, JSON, XML, and other structured formats.

Summary: AI-powered document parsing delivers higher accuracy than rule-based extraction because it understands document context, layout, and structure rather than relying on fixed templates. While rule-based systems work well for standardized forms, they require ongoing maintenance and often fail when document formats change.

In this article, learn the key differences between AI-powered document parsing and rule-based extraction, including how each approach works, where they perform best, and why machine learning models often achieve higher accuracy for document automation across diverse PDF formats.

Summary: The jump from digitized to AI-ready is the next great frontier of digital transformation. This guide explores why traditional OCR pipelines are failing modern AI stacks, the hidden data tax of manual post-processing, and how to build an intelligent infrastructure that turns complex documents into clean, structured JSON.

When it’s time to add new capabilities to a software project, developers and leaders face the decision: build or buy? Juume AI faced this decision during development of the AI compliance platform CapraOne.

Every day, enterprises are extracting, processing, and storing enormous volumes of data from documents such as contracts, medical records, financial filings, and case files. This category of workflows, which Gartner defines as Intelligent Document Processing, covers everything from ingestion and classification through extraction, validation, and downstream use. The infrastructure that workflows run on has always mattered. But for many organizations, especially those in growth mode, the architectural decisions underneath document intelligence have been treated as technical detail rather than strategic risk.

Summary: This article overviews the critical document processing and data extraction layer that inhibits progress on AI projects. By using Apryse SDK to accurately extract data from unstructured documents, developers can fuel AI projects with high quality data that delivers better results and faster ROI.