Open Source Document Management System for Digital Archives (Scanned Documents)

ScanTailor Advanced is the version that merges the features of the ScanTailor Featured and ScanTailor Enhanced versions, brings new ones and fixes.

Evaluate OMR sheets fast and accurately using a scanner 🖨 or your phone 🤳.

A super lightweight image processing algorithm for detection and extraction of overlapped handwritten signatures on scanned documents using OpenCV and scikit-image.

A curated list of awesome projects to simplify and improve paper and document scanning.

The first-ever paper on the Unix shell written by Ken Thompson in 1976 scanned, transcribed, and redistributed with permission

Papermerge DMS core backend, REST API server, and frontend UI

📷 Computer-Vision Demos

Dedoc is a library (service) for automate documents parsing and bringing to a uniform format. It automatically extracts content, logical structure, tables, and meta information from textual electronic documents. (Parse document; Document content extraction; Logical structure extraction; PDF parser; Scanned document parser; DOCX parser; HTML parser

Emacs-assisted PDF document filing

BoxDetect is a Python package based on OpenCV which allows you to easily detect rectangular shapes like character or checkbox boxes on scanned forms.

Make your PDFs look like they were scanned

A document scanner that automatically trims the edge with perspective transform

Android Scanner with OCR support using PDFTron

A tool for extracting text from scanned documents (via OCR), with user-defined post-processing.

ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction

Small utility to prepare scanned documents. Supports separating PDF files by separator pages and removing blank pages.

Documentation for Papermerge DMS - Installation, Help, User Manual, REST API

Segmentation of Scanned Text upto Character Level

Efficient Text Localization Algorithm, Image Inversion Detection of Scanned Documents & Language Identification based on Shape Context and Traditional Computer Vision.