inscriptis – HTML to text conversion library, command line client and Web service

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A python based HTML to text conversion library, command line client and Web service with support for nested tables, a subset of CSS and optional support for providing an annotated output.

Inscriptis is particularly well suited for applications that require high-performance, high-quality (i.e., layout-aware) text representations of HTML content, and will aid knowledge extraction and data science tasks conducted upon Web data.

Please take a look at the Rendering document for a demonstration of inscriptis’ conversion quality.

A Java port of inscriptis 1.x is available here.

This document provides a short introduction to Inscriptis.

Statement of need - why inscriptis?

  1. Inscriptis provides a layout-aware conversion of HTML that more closely resembles the rendering obtained from standard Web browsers and, therefore, better preserves the spatial arrangement of text elements.

    Conversion quality becomes a factor once you need to move beyond simple HTML snippets. Non-specialized approaches and less sophisticated libraries do not correctly interpret HTML semantics and, therefore, fail to properly convert constructs such as itemizations, enumerations, and tables.

    Beautiful Soup’s get_text() function, for example, converts the following HTML enumeration to the string firstsecond.


    Inscriptis, in contrast, not only returns the correct output

    * first
    * second

    but also supports much more complex constructs such as nested tables and also interprets a subset of HTML (e.g., align, valign) and CSS (e.g., display, white-space, margin-top, vertical-align, etc.) attributes that determine the text alignment. Any time the spatial alignment of text is relevant (e.g., for many knowledge extraction tasks, the computation of word embeddings and language models, and sentiment analysis) an accurate HTML to text conversion is essential.

  2. Inscriptis supports annotation rules, i.e., user-provided mappings that allow for annotating the extracted text based on structural and semantic information encoded in HTML tags and attributes used for controlling structure and layout in the original HTML document. These rules might be used to

    • provide downstream knowledge extraction components with additional information that may be leveraged to improve their respective performance.

    • assist manual document annotation processes (e.g., for qualitative analysis or gold standard creation). Inscriptis supports multiple export formats such as XML, annotated HTML and the JSONL format that is used by the open source annotation tool doccano.

    • enabling the use of Inscriptis for tasks such as content extraction (i.e., extract task-specific relevant content from a Web page) which rely on information on the HTML document’s structure.


At the command line:

$ pip install inscriptis

Or, if you don’t have pip installed:

$ easy_install inscriptis

If you want to install from the latest sources, you can do:

$ git clone
$ cd inscriptis
$ python install

Python library

Embedding inscriptis into your code is easy, as outlined below:

import urllib.request
from inscriptis import get_text

url = ""
html = urllib.request.urlopen(url).read().decode('utf-8')

text = get_text(html)

Standalone command line client

The command line client converts HTML files or text retrieved from Web pages to the corresponding text representation.

Command line parameters

The command line client supports the following parameters:

usage: [-h] [-o OUTPUT] [-e ENCODING] [-i] [-d] [-l] [-a] [-r ANNOTATION_RULES] [-p POSTPROCESSOR]
                   [--indentation INDENTATION] [-v]

Convert the given HTML document to text.

positional arguments:
  input                 Html input either from a file or a URL (default:stdin).

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output file (default:stdout).
  -e ENCODING, --encoding ENCODING
                        Input encoding to use (default:utf-8 for files; detected server encoding for Web URLs).
  -i, --display-image-captions
                        Display image captions (default:false).
  -d, --deduplicate-image-captions
                        Deduplicate image captions (default:false).
  -l, --display-link-targets
                        Display link targets (default:false).
  -a, --display-anchor-urls
                        Display anchor URLs (default:false).
                        Path to an optional JSON file containing rules for annotating the retrieved text.
                        Optional component for postprocessing the result (html, surface, xml).
  --indentation INDENTATION
                        How to handle indentation (extended or strict; default: extended).
  -v, --version         display version information

HTML to text conversion

convert the given page to text and output the result to the screen:


convert the file to text and save the output to output.txt:

$ fhgr.html -o fhgr.txt

convert HTML provided via stdin and save the output to output.txt:

$ echo '<body><p>Make it so!</p></body>' | -o output.txt

HTML to annotated text conversion

convert and annotate HTML from a Web page using the provided annotation rules.

Download the example annotation-profile.json and save it to your working directory:

$ -r annotation-profile.json

The annotation rules are specified in annotation-profile.json:

 "h1": ["heading", "h1"],
 "h2": ["heading", "h2"],
 "b": ["emphasis"],
 "div#class=toc": ["table-of-contents"],
 "#class=FactBox": ["fact-box"],
 "#cite": ["citation"]

The dictionary maps an HTML tag and/or attribute to the annotations inscriptis should provide for them. In the example above, for instance, the tag h1 yields the annotations heading and h1, a div tag with a class that contains the value toc results in the annotation table-of-contents, and all tags with a cite attribute are annotated with citation.

Given these annotation rules the HTML file

<b>Chur</b> is the capital and largest town of the Swiss canton of the
Grisons and lies in the Grisonian Rhine Valley.

yields the following JSONL output

{"text": "Chur\n\nChur is the capital and largest town of the Swiss canton
          of the Grisons and lies in the Grisonian Rhine Valley.",
 "label": [[0, 4, "heading"], [0, 4, "h1"], [6, 10, "emphasis"]]}

The provided list of labels contains all annotated text elements with their start index, end index and the assigned label.

Annotation postprocessors

Annotation postprocessors enable the post processing of annotations to formats that are suitable for your particular application. Post processors can be specified with the -p or –postprocessor command line argument:

$ \
        -r ./examples/annotation-profile.json \
        -p surface


{"text": "  Chur\n\n  Chur is the capital and largest town of the Swiss
          canton of the Grisons and lies in the Grisonian Rhine Valley.",
 "label": [[0, 6, "heading"], [8, 14, "emphasis"]],
 "tag": "<heading>Chur</heading>\n\n<emphasis>Chur</emphasis> is the
        capital and largest town of the Swiss canton of the Grisons and
        lies in the Grisonian Rhine Valley."}

Currently, inscriptis supports the following postprocessors:

  • surface: returns a list of mapping between the annotation’s surface form and its label:

       ['heading', 'Chur'],
       ['emphasis': 'Chur']
  • xml: returns an additional annotated text version:

    <?xml version="1.0" encoding="UTF-8" ?>
    <emphasis>Chur</emphasis> is the capital and largest town of the Swiss
    canton of the Grisons and lies in the Grisonian Rhine Valley.
  • html: creates an HTML file which contains the converted text and highlights all annotations as outlined below:

Annotations extracted from the Wikipedia entry for Chur with the `--postprocess html` postprocessor.

Snippet of the rendered HTML file created with the following command line options and annotation rules: --annotation-rules ./wikipedia.json \
            --postprocessor html \

Annotation rules encoded in the wikipedia.json file:

  "h1": ["heading"],
  "h2": ["heading"],
  "h3": ["subheading"],
  "h4": ["subheading"],
  "h5": ["subheading"],
  "i": ["emphasis"],
  "b": ["bold"],
  "table": ["table"],
  "th": ["tableheading"],
  "a": ["link"]

Web Service

The Flask Web Service translates HTML pages to the corresponding plain text.

Additional Requirements

  • python3-flask


Start the inscriptis Web service with the following command:

$ export FLASK_APP="inscriptis.service.web"
$ python3 -m flask run


The Web services receives the HTML file in the request body and returns the corresponding text. The file’s encoding needs to be specified in the Content-Type header (UTF-8 in the example below):

$ curl -X POST  -H "Content-Type: text/html; encoding=UTF8"  \
        --data-binary @test.html  http://localhost:5000/get_text

The service also supports a version call:

$ curl http://localhost:5000/version

Example annotation profiles

The following section provides a number of example annotation profiles illustrating the use of Inscriptis’ annotation support. The examples present the used annotation rules and an image that highlights a snippet with the annotated text on the converted web page, which has been created using the HTML postprocessor as outlined in Section annotation postprocessors.

Wikipedia tables and table metadata

The following annotation rules extract tables from Wikipedia pages, and annotate table headings that are typically used to indicate column or row headings.

   "table": ["table"],
   "th": ["tableheading"],
   "caption": ["caption"]

The figure below outlines an example table from Wikipedia that has been annotated using these rules.

Table and table metadata annotations extracted from the Wikipedia entry for Chur.

References to entities, missing entities and citations from Wikipedia

This profile extracts references to Wikipedia entities, missing entities and citations. Please note that the profile isn’t perfect, since it also annotates [ edit ] links.

   "a#title": ["entity"],
   "a#class=new": ["missing"],
   "class=reference": ["citation"]

The figure shows entities and citations that have been identified on a Wikipedia page using these rules.

Metadata on entries, missing entries and citations extracted from the Wikipedia entry for Chur.

Posts and post metadata from the XDA developer forum

The annotation rules below, extract posts with metadata on the post’s time, user and the user’s job title from the XDA developer forum.

    "article#class=message-body": ["article"],
    "li#class=u-concealed": ["time"],
    "#itemprop=name": ["user-name"],
    "#itemprop=jobTitle": ["user-title"]

The figure illustrates the annotated metadata on posts from the XDA developer forum.

Posts and post metadata extracted from the XDA developer forum.

Code and metadata from Stackoverflow pages

The rules below extracts code and metadata on users and comments from Stackoverflow pages.

   "code": ["code"],
   "#itemprop=dateCreated": ["creation-date"],
   "#class=user-details": ["user"],
   "#class=reputation-score": ["reputation"],
   "#class=comment-date": ["comment-date"],
   "#class=comment-copy": ["comment-comment"]

Applying these rules to a Stackoverflow page on text extraction from HTML yields the following snippet:

Code and metadata from Stackoverflow pages.

Advanced topics

Annotated text

Inscriptis can provide annotations alongside the extracted text which allows downstream components to draw upon semantics that have only been available in the original HTML file.

The extracted text and annotations can be exported in different formats, including the popular JSONL format which is used by doccano.

Example output:

{"text": "Chur\n\nChur is the capital and largest town of the Swiss canton
          of the Grisons and lies in the Grisonian Rhine Valley.",
 "label": [[0, 4, "heading"], [0, 4, "h1"], [6, 10, "emphasis"]]}

The output above is produced, if inscriptis is run with the following annotation rules:

 "h1": ["heading", "h1"],
 "b": ["emphasis"],

The code below demonstrates how inscriptis’ annotation capabilities can be used within a program:

import urllib.request
from inscriptis import get_annotated_text, ParserConfig

url = ""
html = urllib.request.urlopen(url).read().decode('utf-8')

rules = {'h1': ['heading', 'h1'],
         'h2': ['heading', 'h2'],
         'b': ['emphasis'],
         'table': ['table']

output = get_annotated_text(html, ParserConfig(annotation_rules=rules)
print("Text:", output['text'])
print("Annotations:", output['label'])

Fine tuning

The following options are available for fine tuning inscriptis’ HTML rendering:

  1. More rigorous indentation: call inscriptis.get_text() with the parameter indentation=’extended’ to also use indentation for tags such as <div> and <span> that do not provide indentation in their standard definition. This strategy is the default in and many other tools such as Lynx. If you do not want extended indentation you can use the parameter indentation=’standard’ instead.

  2. Overwriting the default CSS definition: inscriptis uses CSS definitions that are maintained in inscriptis.css.CSS for rendering HTML tags. You can override these definitions (and therefore change the rendering) as outlined below:

from lxml.html import fromstring
from inscriptis.css_profiles import CSS_PROFILES, HtmlElement
from inscriptis.html_properties import Display
from inscriptis.model.config import ParserConfig

# create a custom CSS based on the default style sheet and change the
# rendering of `div` and `span` elements
css = CSS_PROFILES['strict'].copy()
css['div'] = HtmlElement(display=Display.block, padding=2)
css['span'] = HtmlElement(prefix=' ', suffix=' ')

html_tree = fromstring(html)
# create a parser using a custom css
config = ParserConfig(css=css)
parser = Inscriptis(html_tree, config)
text = parser.get_text()


A full list of changes can be found in the release notes.



Indices and tables