Text extraction like PyMuPDF, but without the AGPL license. PDFText extracts plain text or structured blocks and lines. It's built on pypdfium2, so it's fast, accurate, and Apache licensed.

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You'll need python 3.9+ first. Then run pip install pdftext.

• Inspect the settings in pdftext/settings.py. You can override any settings with environment variables.

This command will write out a text file with the extracted plain text.

pdftext PDF_PATH --out_path output.txt

• PDF_PATH must be a single pdf file.
• --out_path path to the output txt file. If not specified, will write to stdout.
• --sort will attempt to sort in reading order if specified.
• --keep_hyphens will keep hyphens in the output (they will be stripped and words joined otherwise)
• --pages will specify pages (comma separated) to extract
• --workers specifies the number of parallel workers to use
• --flatten_pdf merges form fields into the PDF

This command outputs structured blocks and lines with font and other information.

pdftext PDF_PATH --out_path output.txt --json

• PDF_PATH must be a single pdf file.
• --out_path path to the output txt file. If not specified, will write to stdout.
• --json specifies json output
• --sort will attempt to sort in reading order if specified.
• --pages will specify pages (comma separated) to extract
• --keep_chars will keep individual characters in the json output
• --workers specifies the number of parallel workers to use
• --flatten_pdf merges form fields into the PDF

The output will be a json list, with each item in the list corresponding to a single page in the input pdf (in order). Each page will include the following keys:

• bbox - the page bbox, in [x1, y1, x2, y2] format
• rotation - how much the page is rotated, in degrees (0, 90, 180, or 270)
• page - the index of the page
• blocks - the blocks that make up the text in the pdf. Approximately equal to a paragraph.
  • bbox - the block bbox, in [x1, y1, x2, y2] format
  • lines - the lines inside the block
    • bbox - the line bbox, in [x1, y1, x2, y2] format
    • spans - the individual text spans in the line (text spans have the same font/weight/etc)
      • text - the text in the span, encoded in utf-8
      • rotation - how much the span is rotated, in degrees
      • bbox - the span bbox, in [x1, y1, x2, y2] format
      • char_start_idx - the start index of the first span character in the pdf
      • char_end_idx - the end index of the last span character in the pdf
      • font this is font info straight from the pdf, see this pdfium code
        • size - the size of the font used for the text
        • weight - font weight
        • name - font name, may be None
        • flags - font flags, in the format of the PDF spec 1.7 Section 5.7.1 Font Descriptor Flags

If the pdf is rotated, the bboxes will be relative to the rotated page (they're rotated after being extracted).

Extract plain text:

from pdftext.extraction import plain_text_output

text = plain_text_output(PDF_PATH, sort=False, hyphens=False, page_range=[1,2,3]) # Optional arguments explained above

Extract structured blocks and lines:

from pdftext.extraction import dictionary_output

text = dictionary_output(PDF_PATH, sort=False, page_range=[1,2,3], keep_chars=False) # Optional arguments explained above

If you want more customization, check out the pdftext.extraction._get_pages function for a starting point to dig deeper. pdftext is a pretty thin wrapper around pypdfium2, so you might want to look at the documentation for that as well.

I benchmarked extraction speed and accuracy of pymupdf, pdfplumber, and pdftext. I chose pymupdf because it extracts blocks and lines. Pdfplumber extracts words and bboxes. I did not benchmark pypdf, even though it is a great library, because it doesn't provide individual character/line/block and bbox information.

Here are the scores, run on an M1 Macbook, without multiprocessing:

pdftext is approximately 2x slower than using pypdfium2 alone (if you were to extract all the same character information).

There are additional benchmarks for pypdfium2 and other tools here.

I used a benchmark set of 200 pdfs extracted from common crawl, then processed by a team at HuggingFace.

For each library, I used a detailed extraction method, to pull out font information, as well as just the words. This ensured we were comparing similar performance numbers. I formatted the text similarly when extracting - newlines after lines, and double newlines after blocks. For pdfplumber, I could only do the newlines after lines, since it doesn't recognize blocks.

For the alignment score, I extracted the text, then used the rapidfuzz library to find the alignment percentage. I used the text extracted by pymupdf as the pseudo-ground truth.

You can run the benchmarks yourself. To do so, you have to first install pdftext manually. The install assumes you have poetry and Python 3.9+ installed.

git clone https://github.com/VikParuchuri/pdftext.git
cd pdftext
poetry install
python benchmark.py # Will download the benchmark pdfs automatically

The benchmark script has a few options:

• --max this controls the maximum number of pdfs to benchmark
• --result_path a folder to save the results. A file called results.json will be created in the folder.
• --pdftext_only skip running pdfplumber, which can be slow.

PDFText is a very light wrapper around pypdfium2. It first uses pypdfium2 to extract characters in order, along with font and other information. Then it uses a simple decision tree algorithm to group characters into lines and blocks. It does some simple postprocessing to clean up the text.

This is built on some amazing open source work, including:

• pypdfium2
• scikit-learn
• pypdf for very thorough and fair benchmarks

Thank you to the pymupdf devs for creating such a great library - I just wish it had a simpler license!