Local-first document scanning for privacy-conscious users.
e-Doc.me is a simple, open-source Python project that allows users to digitize documents entirely on their own machines, without uploading images or files to cloud services.
The core goal of the project is digital privacy and data security:
your documents should never leave your computer.
Many document scanning tools rely on cloud-based processing, often without making this explicit. This means sensitive documents — such as IDs, contracts, academic records, or medical paperwork — may be uploaded, processed, stored, or analyzed by third parties.
e-Doc.me follows a different approach:
- Local-first processing
- No cloud uploads
- No external APIs
- No telemetry or tracking
All document handling happens offline and locally, under the user’s control.
This initial version provides a minimal and transparent document-scanning pipeline:
- Load an image from the local filesystem
- Manually crop the document region
- Apply perspective correction (warp to A4 format)
- Export the result as a PDF
The limited scope is intentional, prioritizing simplicity, auditability, and privacy guarantees.
This version of e-Doc.me does not:
- Upload files to any server
- Perform OCR (optical character recognition)
- Automatically detect document edges
- Support multi-page documents
- Encrypt output files
Future features will only be added if they preserve the local-only, privacy-by-design philosophy of the project.
- Input files remain on the user’s machine
- Processing is done locally using open-source libraries
- No data is transmitted externally
- No analytics, logging, or tracking mechanisms are implemented
If the program runs offline, it functions fully.
Clone the repository:
git clone https://github.com/Starblessed/e-Doc.me.git
cd e-Doc.meInstall dependencies:
pip install -r requirements.txtRun the main script according to the project structure.
e-Doc.me is intended for users who:
- Handle sensitive or personal documents
- Prefer local processing over cloud-based tools
- Want transparency and control over their data
- Value open-source and inspectable software
The project also serves as an academic and professional exploration of privacy-preserving data processing, security-by-design, and digital governance principles.
- Early-stage / experimental
- Functional but minimal
- Actively evolving
Feedback, audits, and contributions are welcome.
This project is open-source. See the LICENSE file for details.