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Automate the workflow around ADF scanning, OCR and PDF creation

Project description

adf2pdf - a tool that turns a batch of paper pages into a PDF with a text layer. By default, it detects empty pages (as they may easily occur during duplex scanning) and excludes them from the OCR and the resulting PDF.

For that, it uses Sane's scanimage for the scanning, Tesseract for the [optical character recognition] (OCR), and the Python packages img2pdf, Pillow (PIL) and PyPDF2 for some image-processing tasks and PDF mangling.

Example:

$ adf2pdf contract-xyz.pdf

2017, Georg Sauthoff mail@gms.tf

Features

  • Automatic document feed (ADF) support
  • Fast empty page detection
  • Overlaying of scanning, image processing, OCR and PDF creation to minimize the total runtime
  • Fast creation of small PDFs using the fine img2pdf package
  • Only use of safe compression methods, i.e. no error-prone symbol segmentation style compression like JBIG2 or JB2 that is used in Xerox photocopiers and the DjVu format.

Install Instructions

Adf2pdf can be directly installed with pip, e.g.

$ pip3 install --user adf2pdf

or

$ pip3 install adf2pdf

See also the PyPI adf2pdf project page.

Alternatively, the Python file adf2pdf.py can be directly executed in a cloned repository, e.g.:

$ ./adf2pdf.py report.pdf

In addition to that, one can install the development version from a cloned work-tree like this:

$ pip3 install --user .

Hardware Requirements

A scanner with automatic document feed (ADF) that is supported by Sane. For example, the Fujitsu ScanSnap S1500 works well. That model supports duplex scanning, which is quite convenient.

Example continued

Running adf2pdf for a 7 page example document takes 150 seconds on an i7-6600U (Intel Skylake, 4 cores) CPU (using the ADF of the Fujitsu ScanSnap S1500). With the defaults, adf2pdf calls scanimage for duplex scanning into 600 dpi lineart (black and white) images. In this example, 6 pages are empty and thus automatically excluded, i.e. the resulting PDF then just contains 8 pages.

The resulting PDF contains a text layer from the OCR such that one can search and copy'n'paste some text. It is 1.1 MiB big, i.e. a page is stored in 132 KiB, on average.

Software Requirements

The script assumes Tesseract version 4, by default. Version 3 can be used as well, but the new neural network system in Tesseract 4 just performs magnitudes better than the old OCR model. As of mid 2018, there is no stable version 4, yet, but since the beta version is so much better at OCR I can't recommend it enough over the stable version 3.

Tesseract 4 notes:

  • Build instructions - warning: if you miss the autoconf-archive dependency you'll get weird autoconf error messages
  • Data files - you need the training data for your languages of choice and the OSD data

Python packages:

  • img2pdf (not packaged for Fedora, yet) - version 0.2.4 works fine
  • Pillow (PIL) (Fedora package: python3-pillow-devel)
  • PyPDF2 (Fedora package: python3-PyPDF2)

Project details


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