Originally written by Jens Gulden — see AUTHORS for more information.
The entire unpaper project is licensed under GNU GPL v2.
Some of the individual files are licensed under the MIT or Apache 2.0 licenses.
Each file contains an SPDX license header
specifying its license. The text of all three licenses is available under
LICENSES.
unpaper is a post-processing tool for scanned sheets of paper,
especially for book pages that have been scanned from previously
created photocopies. The main purpose is to make scanned book pages
better readable on screen after conversion to PDF. Additionally,
unpaper might be useful to enhance the quality of scanned pages
before performing optical character recognition (OCR).
unpaper tries to clean scanned images by removing dark edges that
appeared through scanning or copying on areas outside the actual page
content (e.g. dark areas between the left-hand-side and the
right-hand-side of a double- sided book-page scan).
The program also tries to detect misaligned centering and rotation of pages and will automatically straighten each page by rotating it to the correct angle. This process is called "deskewing".
Note that the automatic processing will sometimes fail. It is always a good idea to manually control the results of unpaper and adjust the parameter settings according to the requirements of the input. Each processing step can also be disabled individually for each sheet.
See further documentation for the supported file formats notes.
The only hard dependency of unpaper is ffmpeg, which is used for
file input and output.
unpaper uses the Meson Build system, which
can be installed using Python's package manage (pip3 or pip):
unpaper$ pip3 install --user 'meson >= 0.57' 'sphinx >= 3.4'
unpaper$ CFLAGS="-march=native" meson --buildtype=debugoptimized builddir
unpaper$ meson compile -C builddir
You can pass required optimization flags when creating the meson build
directory in the CFLAGS environment variable. Usage of Link-Time
Optimizations (Meson option -Db_lto=true) is recommended if
available.
Further optimizations such as -ftracer and -ftree-vectorize are
thought to work, but their effect has not been evaluated so your
mileage may vary.
Tests depend on pytest and pillow, which will be auto-detected by
Meson.
The project includes configuration for pre-commit
which is integrated with GitHub Actions CI. If you're using git for
devleopment, you can install it with
pip install pre-commit && pre-commit --install.
Using Sapling with this repository is possible and diffs can be reviewed as a stack.
You can find more information on the basic concepts and the image processing in the available documentation.