Update README.md
Browse files# Document OCR & Data Collection Tool
## Overview
This tool is an OCR (Optical Character Recognition) system built to convert scanned or photographed business documents — invoices, purchase orders, billing forms, and similar paperwork — into structured, usable data. It is built on top of the Quinn model and designed as a **data collection and automation layer** for businesses that currently rely on manual data entry.
Rather than functioning as a standalone document viewer, this tool is intended to sit between paper/PDF documents and the systems businesses already use (spreadsheets, ERPs, billing software), acting as a lightweight connector that pulls structured data out of unstructured documents.
## What Problem This Solves
Many small and mid-sized businesses — construction companies, B2B service providers, financial/tax service firms, and administrative-heavy operations — spend significant time manually transcribing data from paper documents into spreadsheets or internal systems. This is slow, error-prone, and hard to scale as document volume grows.
This tool is built for two types of teams:
- **Fresh-start teams** with no existing data system, who want to skip manual entry entirely from day one.
- **Transitioning teams** who already use a system (e.g., SAP, QuickBooks, an internal database) and are trying to modernize or migrate, and need a third-party tool to bridge the gap without overhauling their entire stack.
## Current Capabilities
- Extracts text and key fields from scanned documents and images (invoices, orders, billing forms)
- Converts unstructured document content into structured, exportable data (e.g., spreadsheet rows)
- Built on the Quinn model as its underlying recognition engine
## Planned Capabilities (Roadmap)
- **Configurable field extraction** — users define which fields matter for their business (e.g., vendor name, invoice number, total, PO number, due date), rather than relying on hardcoded, industry-specific logic.
- **Anomaly detection & flagging** — users define their own validation rules on top of extracted data, such as:
- Missing required fields
- Mismatched totals (e.g., invoice total vs. sum of line items)
- Duplicate entries
- Values outside expected ranges
- Formatting or compliance issues
- **Exception reporting** — flagged items are separated from clean data, so teams can quickly see what needs review, rather than manually re-checking every document.
The goal is a system where each business builds its own "perspective" on its data — defining what a normal record looks like and what counts as an issue — while the OCR and extraction pipeline stays the same underneath.
## Positioning: A Connector, Not a Replacement
This tool is designed to act similarly to an external connector (in the spirit of an MCP-style integration) — plugging into a company's existing workflow and systems to feed them clean, structured data, rather than requiring a business to replace their current tools. This makes it easier to adopt for companies who are cautious about ripping out existing systems but still need help getting off manual entry.
## Who This Is For (Initial Focus)
- Small construction companies handling B2B invoicing
- Financial and tax service providers needing organized records for compliance purposes
- Teams manually pulling and reviewing order data from systems like SAP for closing, canceling, or reconciliation workflows
## Status
This is an early-stage tool. The OCR extraction component is functional; anomaly detection and configurable rule-building are in active development. Early testers and pilot partners are being used to gather real-world data and feedback rather than as a revenue source at this stage.
## Limitations & Notes
- Accuracy may vary depending on scan quality, handwriting, and document formatting. Testing across a range of real document samples is recommended before production use.
- This tool does not make compliance determinations (e.g., tax or regulatory decisions) — it organizes and structures data to make those downstream processes easier, but does not replace professional review.
- Documents containing sensitive or regulated data (e.g., medical billing, financial records) should be handled according to applicable data privacy and compliance requirements (e.g., HIPAA where relevant).
## Feedback
This project is in active development. Feedback from early testers on accuracy, usability, and desired fields/rules is welcomed and directly shapes the next phase of development.