Metadata-Version: 2.1
Name: scanprep
Version: 1.0.2
Summary: Small utility to prepare scanned documents. Supports separating PDF files by separator pages and removing blank pages.
Home-page: https://github.com/baltpeter/scanprep
Author: Benjamin Altpeter
Author-email: hi@bn.al
License: UNKNOWN
Description: # scanprep – Prepare scanned PDF documents
        
        > Small utility to prepare scanned documents. Supports separating PDF files by separator pages and removing blank pages.
        
        <!-- TODO: GIF showing how to use scanprep -->
        
        Scanprep can be used to prepare scanned documents for further processing with existing tools (like the great [OCRmyPDF](https://github.com/jbarlow83/OCRmyPDF)) or directly for archival. It allows splitting multiple documents that were scanned in a single batch into multiple files. In addition, it can also remove blank pages from the output (this is especially helpful if using a duplex scanner).
        
        For document separation, separator pages need to be inserted between the different documents before scanning. These pages tell the program where to split. You can either use the [included separator page](/separator-page.pdf) or create your own. The separator page simply needs to have a barcode that encodes the text `SCANPREP_SEP` (you can use any [barcode type supported by zbar](http://zbar.sourceforge.net/about.html)).
        
        ## Installation
        
        ### Via Snap
        
        You can install scanprep from the [Snap Store](https://snapcraft.io/scanprep):
        
        ```sh
        snap install scanprep
        
        scanprep -h
        ```
        
        ### Via PyPI
        
        You can install scanprep using `pip` (consider doing that in a venv):
        
        ```sh
        pip3 install scanprep
        
        # If you see an error like "ImportError: Unable to find zbar shared library", you need to install zbar yourself. See: https://pypi.org/project/pyzbar/
        scanprep -h
        ```
        
        ### From source
        
        To install scanprep from source, clone this repository and install the dependencies:
        
        ```sh
        git clone https://github.com/baltpeter/scanprep.git
        cd scanprep
        pip3 install -r requirements.txt # You may want to do this in a venv.
        # You may also need to install the zbar shared library. See: https://pypi.org/project/pyzbar/
        
        python3 scanprep/scanprep.py -h
        ```
        
        ## Usage
        
        Most simply, you can run scanprep via `scanprep <filename.pdf>`. This will process the input file and output the results into your current working directory. To specify a different output directory, use `scanprep <filename.pdf> <output_directory>`.  
        The output files will be called `0-<filename.pdf>`, `1-<filename.pdf>`, and so on.
        
        By default, both page separation and blank page removal will be performed. To turn them off, use `--no-page-separation` or `--no-blank-removal`, respectively.
        
        Use `scanprep -h` to show the help:
        
        ```
        usage: scanprep [-h] [--page-separation] [--blank-removal] input_pdf [output_dir]
        
        positional arguments:
          input_pdf             The PDF document to process.
          output_dir            The directory where the output documents will be saved. (defaults to the
                                current directory)
        
        optional arguments:
          -h, --help            show this help message and exit
          --page-separation, --no-page-separation
                                Do (or do not) split document into separate files by the included
                                separator pages. (default yes)
          --blank-removal, --no-blank-removal
                                Do (or do not) remove empty pages from the output. (default yes)
        ```
        
        ## License
        
        Scanprep is licensed under the MIT license, see the [`LICENSE`](/LICENSE) file for details. Issues and pull requests are welcome!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
