DOCENT

DOCENT is a desktop-first document operations workspace built with Electron, React, and TypeScript. It inspects selected files locally and is designed to demonstrate a more professional, security-conscious document intake flow than qomplement's current website positioning.

Latest update

• Added drag-and-drop document intake directly into the staging queue for faster local testing from Finder
• Added queue upsert behavior by file path so re-dropping or re-selecting a file refreshes the existing row instead of duplicating it
• Added safer operator-facing error handling around local inspection, report export, and file reveal actions
• Added live inspection progress in the desktop UI, including current file and OCR page progress
• Added bounded multi-page OCR fallback for weak-text PDFs with a hard three-page cap
• Added local scan cleanup before OCR to improve recognition on scanned PDFs and image documents
• Added richer OCR trace details to the review UI, including processed pages, preprocessing mode, duration, confidence, and recovered text
• Added OCR-safe cache handling so language data stays in the app-data directory instead of the repo
• Added local workspace persistence so staged documents, filters, review state, and notes survive restart
• Added a review workflow with status tracking, notes, and exported review metadata
• Added the first structured extraction workflow for W-9 detection and schema mapping
• Added tests for the local document intelligence module and a npm test command

Problem

Document-heavy teams still lose time on work that should be structured but usually is not. Internal operations, finance, healthcare, legal support, logistics, and back-office teams routinely receive PDFs, scans, spreadsheets, and forms that must be reviewed, interpreted, re-entered, approved, and delivered into another system. The file is only one part of the workflow. The real operational burden is the handoff between intake, validation, decision-making, and downstream execution.

This is a real problem because:

• Important business data often arrives inside unstructured or semi-structured documents
• Teams still manually copy values between files, portals, and internal systems
• Errors in document handling create compliance, financial, and operational risk
• Most tools focus on extraction alone, while real teams also need review, trust, and controlled execution
• Security-sensitive organizations do not want every document workflow to begin with immediate cloud upload

Why this project exists

DOCENT is built around a simple thesis: document automation is not only an OCR problem. It is an operational control problem. Teams need a secure and professional workspace where documents can be staged, inspected, validated, and eventually routed into a governed workflow. That is the gap this project is trying to address.

What it does today

• Select documents through the native Electron file picker
• Drag documents from Finder directly into the staging queue for local inspection
• Parse PDF files locally on-device
• Run bounded multi-page OCR locally when PDF text extraction is weak
• Run local OCR directly on PNG and JPEG files with scan cleanup before recognition
• Show live inspection progress while DOCENT parses files and steps through OCR pages
• Show real page counts, document metadata, SHA-256 fingerprints, and extracted preview text
• Surface OCR status, source, confidence, runtime, preprocessing mode, processed pages, and recovered text in the operator workspace
• Infer likely document type and handling sensitivity from parsed text without leaving the device
• Persist the local workspace, including review decisions and notes, in the app data directory
• Track document review state across Pending, Needs review, Approved, and Rejected
• Detect W-9 templates locally and surface a first structured extraction panel
• Search the queue by filename, metadata, path, or extracted text
• Flag duplicate documents locally through SHA-256 fingerprint matching
• Export a local JSON inspection report for review or audit handoff
• Keep non-PDF files in a metadata-only staging flow
• Present a market review and product critique for qomplement based on public sources

What it does not do yet

• No backend API
• No cloud upload
• No high-throughput OCR queue for large batches
• No field mapping or document filling
• No user accounts, auth, or storage layer

Future scope

The current repository is intentionally narrow. It proves the desktop intake and local inspection layer first. The most useful next steps are:

• Add structured field extraction for common document classes
• Expand OCR coverage to TIFF, larger scan sets, and smarter per-document OCR scheduling
• Expand the local classification heuristics into document-specific extraction workflows
• Add side-by-side review and correction before export
• Add templates for invoices, tax forms, onboarding packets, and internal forms
• Add export adapters for CRMs, ERPs, ticketing systems, and internal APIs
• Add a backend job system only when there is a clear need for remote processing, audit trails, and team collaboration
• Add role-based access controls, secure storage, and full audit logging if the product evolves into a networked platform

Run locally

npm install
npm run dev

Build and launch

npm run build
npm start

Test

npm test

OCR note

OCR recognition runs locally. On first OCR use, the Tesseract English language data may be downloaded once and then cached in the app-data directory for reuse. Weak-text PDFs are currently OCR'd with a hard cap of three pages, and image OCR applies local scan cleanup before recognition. Document contents are not uploaded for OCR processing.

Test documents

Public sample forms are included in samples/README.md.

Security posture

• Renderer sandbox is enabled
• contextIsolation is enabled
• nodeIntegration is disabled
• Navigation and permission requests are denied by default
• External links are brokered through the main process only
• The preload bridge is minimal
• PDF inspection happens locally in the Electron main process

See SECURITY.md for the current security boundary of the desktop application.

License

This project is licensed under the MIT License. See LICENSE.