The PDF Content Converter is a tool for converting PDF text as well as structural features into a pandas dataframe, written natively in Python. It retrieves information about textual content, fonts, positions, character frequencies and surrounding visual PDF elements.

• Pass the path of the PDF file which is wanted to be converted to PDFContentConverter.
• Call the function pdf2pandas(). The PDF content is then returned as a pandas dataframe.
• Media boxes of a PDF can be accessed using get_media_boxes(), the page count over get_page_count() and the document text using pdf2text().
• Using the convert() function, the pandas dataframe, textual document content, media boxes and page count are returned as a dictionary.

Example call:

converter = PDFContentConverter(pdf)
result = converter.pdf2pandas()

A more detailed example usage is also given in Tester.py.

• PDFContentConverter.py: contains the PDFContentConverter class for converting PDF documents.
• util:
  • constants: paths to input and output data, pdfminer parameters
  • StorageUtil: store/load functionalities
• Tester.py: Python script for testing the PDFContentConverter
• csv: example csv output files for tests
• pdf: example pdf input files for tests

The output containing the converted PDF data is stored as pandas dataframe. The different PDF elements are stored as rows. The dataframe contains the following columns:

• id: unique identifier of the PDF element
• page: page number, starting with 0
• text: text of the PDF element
• x_0: left x coordinate
• x_1: right x coordinate
• y_0: top y coordinate
• y_1: bottom y coordinate
• pos_x: center x coordinate
• pos_y: center y coordinate
• abs_pos: tuple containing a page independent representation of (pos_x,pos_y) coordinates
• original_font: font as extracted by pdfminer
• font_name: name of the font extracted from original_font
• code: font code as provided by pdfminer
• bold: factor 1 indicating that a text is bold and 0 otherwise
• italic: factor 1 indicating that a text is italic and 0 otherwise
• font_size: size of the text in points
• masked: text with numeric content substituted as #
• frequency_hist: histogram of character type frequencies in a text, stored as a tuple containing percentages of textual, numerical, text symbolic and other symbols
• len_text: number of characters
• n_tokens: number of words
• tag: tag for key-value pair extractions, indicating keys or values based on simple heuristics
• box: box extracted by pdfminer Layout Analysis
• in_element_ids: contains IDs of surrounding visual elements such as rectangles or lists. They are stored as a list [left, right, top, bottom]. -1 is indicating that there is no adjacent visual element.
• in_element: indicates based on in_element_ids whether an element is stored in a visual rectangle representation (stored as "rectangle") or not (stored as "none").

Additionally, a dictionary is returned containing the following entries, which can be used to transform the absolute CSV coordinates:

• x0: Left x page crop box coordinate
• x1: Right x page crop box coordinate
• y0: Top y page crop box coordinate
• y1: Bottom y page crop box coordinate
• x0page: Left x page coordinate
• x1page: Right x page coordinate
• y0page: Top y page coordinate
• y1page: Bottom y page coordinate

Both are returned in a dictionary when using convert(). The dataframe is stored as "content", the page characteristics as "media_boxes", the textual content as "text" and the number of pages as "page_count".

• This work is built on top of the pdfminer project https://github.com/euske/pdfminer.
• Example PDFs are obtained from the ICDAR Table Recognition Challenge 2013 https://roundtrippdf.com/en/data-extraction/pdf-table-recognition-dataset/.

• Michael Benedikt Aigner
• Florian Preis