Manual document processing can be slow, inconsistent, and difficult to scale. Teams often need to review image quality, combine scanned pages, identify document fields, and manually enter line-item data into internal systems.
The platform automates this workflow through a modern drag-and-drop web interface built with Python Flask, Google Cloud Document AI, and OpenCV. Users can upload multiple document images, validate their visual quality, combine them into organized multi-page PDF files, and extract structured information through a streamlined processing experience.
A size-independent blur detection pipeline uses Tenengrad, Brenner, and edge-density analysis to identify unclear or low-quality uploads across different image resolutions. This helps prevent poor scans from entering the extraction workflow and reduces the likelihood of incomplete or inaccurate results.
The document extraction layer converts recognized content into predefined structured schemas, allowing organizations to capture important fields consistently. It also detects tabular content and generates organized line-item records for invoices, purchase orders, receipts, forms, and other transaction-focused documents.
The platform helps businesses reduce manual data entry, improve document quality control, accelerate processing workflows, and integrate extracted information into accounting systems, ERP platforms, approval processes, or custom business applications.
Need an automated OCR, document extraction, or intelligent data-capture platform? Let’s build a production-ready solution around your documents and operational workflows.
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