PDFSegmenter 0.1

This library builds a graph-representation of the content of PDFs. The graph is then clustered, resulting page segments are classified and returned. Tables are retrieved formatted as a CSV.

This library builds a graph-representation of the content of PDFs. The graph is then clustered, resulting page segments are classified and returned. Tables are retrieved formatted in a CSV-style.

How-to

• Pass the path of the PDF file (as a string) which is wanted to be converted to PDFSegmenter.

• Call the function segment_document().

• The function get_labeled_graphs() returns page-wise document graph representations as a list of networkx graphs. The labels indicate a clustering assignment.

• segments2json() returns a JSON representation of the segmented document.

• segments2text() returns a textual representation of the segmented document. This can be either annotated (lists, text and tables are supported) or not and controlled via the boolean parameter annotate.

• Media boxes of a PDF can be accessed using get_media_boxes(), the page count over get_page_count().

Example call:

segmenter = PDFSegmenter(pdf)

segmenter.segment_document()

result = segmenter.segments2json()

text = segmenter.segments2text()

graphs = get_labeled_graphs()

A file is the only parameter mandatory for the page segmentation.

Beside the graph conversion, media boxes of a document can be accessed using get_media_boxes() and the page count over get_page_count().

JSON

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Annotated text

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Settings

Clustering

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Merging

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Classification

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Graph

General parameters:

• file: file name

• merge_boxes: indicating if PDF text boxes should be graph nodes, based on visual rectangles present in documents.

• regress_parameters: indicating if graph parameters are regressed or used as a priori optimized default ones.

Edge restrictions:

• use_font: differing font size

• use_width: differing width

• use_rect: nodes contained in differing visual structures

• use_horizontal_overlap: indicating if horizontal edges should be built on overlap. If not, default deltas are used.

• use_vertical_overlap: indicating if vertical edges should be built on overlap. If not, default deltas are used.

Edge thresholds:

• page_ratio_x: maximal relative horizontal distance of two nodes where an edge can be created

• page_ratio_y: maximal relative vertical distance of two nodes where an edge can be created

• x_eps: alignment epsilon for vertical edges in points if use_horizontal_overlap is not enabled

• y_eps: alignment epsilon for horizontal edges in points if use_vertical_overlap is not enabled

• font_eps_h: indicates how much font sizes of nodes are allowed to differ as a constraint for building horizontal edges when use_font is enabled

• font_eps_v: indicates how much font sizes of nodes are allowed to differ as a constraint for building vertical edges when use_font is enabled

• width_pct_eps: relative width difference of nodes as a condition for vertical edges if use_width is enabled

• width_page_eps: indicating at which maximal width of a node the width should act as an edge condition if use_width is enabled

Project Structure

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Output Format

JSON

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Text

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Graph

As a result, a list of networkx graphs is returned.

Each graph encapsulates a structured representation of a single page.

Edges are attributed with the following features:

• direction: shows the direction of an edge.

  * v: Vertical edge

  * h: Horizontal edge

  * l: Rectangular loop. This represents a novel concept encapsulating structural characteristics of document segments by observing if two different paths end up in the same node.

• length: Scaled length of an edge

• lengthx_phys: Horizontal edge length

• lengthy_phys: Vertical edge length

• weight: Scaled total length

All nodes contain the following content attributes:

• 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”).

• is_loop: indicates whether or not a node is connected via a rectangular loop

The media boxes possess the following entries in a dictionary:

• 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

Acknowledgements

• Example PDFs are obtained from the ICDAR Table Recognition Challenge 2013 https://roundtrippdf.com/en/data-extraction/pdf-table-recognition-dataset/.

Authors

• Michael Benedikt Aigner

• Florian Preis