Fixes mojibake and other glitches in Unicode text, after the fact.

Overview

ftfy: fixes text for you

Travis PyPI package Docs

>>> print(fix_encoding("(ง'⌣')ง"))
(ง'⌣')ง

Full documentation: https://ftfy.readthedocs.org

Testimonials

  • “My life is livable again!” — @planarrowspace
  • “A handy piece of magic” — @simonw
  • “Saved me a large amount of frustrating dev work” — @iancal
  • “ftfy did the right thing right away, with no faffing about. Excellent work, solving a very tricky real-world (whole-world!) problem.” — Brennan Young
  • “Hat mir die Tage geholfen. Im Übrigen bin ich der Meinung, dass wir keine komplexen Maschinen mit Computern bauen sollten solange wir nicht einmal Umlaute sicher verarbeiten können. :D” — Bruno Ranieri
  • “I have no idea when I’m gonna need this, but I’m definitely bookmarking it.” — /u/ocrow
  • “9.2/10” — pylint

Developed at Luminoso

Luminoso makes groundbreaking software for text analytics that really understands what words mean, in many languages. Our software is used by enterprise customers such as Sony, Intel, Mars, and Scotts, and it's built on Python and open-source technologies.

We use ftfy every day at Luminoso, because the first step in understanding text is making sure it has the correct characters in it!

Luminoso is growing fast and hiring. If you're interested in joining us, take a look at our careers page.

What it does

ftfy fixes Unicode that's broken in various ways.

The goal of ftfy is to take in bad Unicode and output good Unicode, for use in your Unicode-aware code. This is different from taking in non-Unicode and outputting Unicode, which is not a goal of ftfy. It also isn't designed to protect you from having to write Unicode-aware code. ftfy helps those who help themselves.

Of course you're better off if your input is decoded properly and has no glitches. But you often don't have any control over your input; it's someone else's mistake, but it's your problem now.

ftfy will do everything it can to fix the problem.

Mojibake

The most interesting kind of brokenness that ftfy will fix is when someone has encoded Unicode with one standard and decoded it with a different one. This often shows up as characters that turn into nonsense sequences (called "mojibake"):

  • The word schön might appear as schön.
  • An em dash () might appear as —.
  • Text that was meant to be enclosed in quotation marks might end up instead enclosed in “ and â€<9d>, where <9d> represents an unprintable character.

ftfy uses heuristics to detect and undo this kind of mojibake, with a very low rate of false positives.

This part of ftfy now has an unofficial Web implementation by simonw: https://ftfy.now.sh/

Examples

fix_text is the main function of ftfy. This section is meant to give you a taste of the things it can do. fix_encoding is the more specific function that only fixes mojibake.

Please read the documentation for more information on what ftfy does, and how to configure it for your needs.

>>> print(fix_text('This text should be in “quotesâ€\x9d.'))
This text should be in "quotes".

>>> print(fix_text('ünicode'))
ünicode

>>> print(fix_text('Broken text&hellip; it&#x2019;s flubberific!',
...                normalization='NFKC'))
Broken text... it's flubberific!

>>> print(fix_text('HTML entities &lt;3'))
HTML entities <3

>>> print(fix_text('<em>HTML entities in HTML &lt;3</em>'))
<em>HTML entities in HTML &lt;3</em>

>>> print(fix_text('\001\033[36;44mI&#x92;m blue, da ba dee da ba '
...               'doo&#133;\033[0m', normalization='NFKC'))
I'm blue, da ba dee da ba doo...

>>> print(fix_text('LOUD NOISES'))
LOUD NOISES

>>> print(fix_text('LOUD NOISES', fix_character_width=False))
LOUD NOISES

Installing

ftfy is a Python 3 package that can be installed using pip:

pip install ftfy

(Or use pip3 install ftfy on systems where Python 2 and 3 are both globally installed and pip refers to Python 2.)

If you're on Python 2.7, you can install an older version:

pip install 'ftfy<5'

You can also clone this Git repository and install it with python setup.py install.

Who maintains ftfy?

I'm Robyn Speer ([email protected]). I develop this tool as part of my text-understanding company, Luminoso, where it has proven essential.

Luminoso provides ftfy as free, open source software under the extremely permissive MIT license.

You can report bugs regarding ftfy on GitHub and we'll handle them.

Citing ftfy

ftfy has been used as a crucial data processing step in major NLP research.

It's important to give credit appropriately to everyone whose work you build on in research. This includes software, not just high-status contributions such as mathematical models. All I ask when you use ftfy for research is that you cite it.

ftfy has a citable record on Zenodo. A citation of ftfy may look like this:

Robyn Speer. (2019). ftfy (Version 5.5). Zenodo.
http://doi.org/10.5281/zenodo.2591652

In BibTeX format, the citation is::

@misc{speer-2019-ftfy,
  author       = {Robyn Speer},
  title        = {ftfy},
  note         = {Version 5.5},
  year         = 2019,
  howpublished = {Zenodo},
  doi          = {10.5281/zenodo.2591652},
  url          = {https://doi.org/10.5281/zenodo.2591652}
}
Comments
  • Bump certifi from 2021.10.8 to 2022.12.7

    Bump certifi from 2021.10.8 to 2022.12.7

    Bumps certifi from 2021.10.8 to 2022.12.7.

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Performance improvements using google-re2. 2 times faster to run fix_text()

    Performance improvements using google-re2. 2 times faster to run fix_text()

    Hi, thanks for the great lib!

    In our real time inference server, we are using ftfy to clean inputs coming from users. We noticed that processing time can be huge with a lot of text. So I run this little experiment to usegoogle-re2 which is a regex engine optimized for performance. On my test file of 10000 lines, I was able to clean the text, 2 times faster. On a run of 10, I'm getting 16.15 seconds with vanilla ftfy and 8.71 seconds with the optimizations made in this PR.

    As is, this PR is not mergable, its implies a big change for the lib. I think it should be better to have a way of choosing regex engine. If you are interested in merging it, I can make the necessary changes. I'm publishing it just for you and the community to know it's possible and what the expected outcomes can be. Of course, I made sure than all the tests are green.

    Anyone can test it by installing this branch pip install git+https://@github.com/ablanchard/[email protected]

    Notes on the PR :

    • re.VERBOSE is not supported by google-re2. To keep comments and line returns, I process it by "hand" using a regex. Bit of a hack but it works.
    • lookahead and lookbehind arenot supported by google-re2 so I splited the UTF8 detector and the a grave regex in 2 separate regexes in order to keep the same behavior. Meaning that UTF8_DETECTOR_RE.search() doesn't return the same results as before so you have to call the method utf8_detector(). The same idea goes for the sub method.
    • By default google-re2 uses utf8 for encoding regexes so to use binary string you have to pass options=LATIN_OPTIONS
    • I didn't migrate the surrogates for utf-16. In my understanding,it's not supported by google-re2. So I left it as it was.

    PS: Code used for the benchmark:

    import time
    import ftfy
    import pandas as pd
    import sys
    
    df = pd.read_csv(sys.argv[1])
    texts = df['input_text'].tolist()
    start_time = time.time()
    res = [ftfy.fix_text(text) for text in texts]
    print(time.time() - start_time)
    
    opened by ablanchard 0
  • Restore Python 36 support

    Restore Python 36 support

    Hi! There is not much that prohibits to still support Python 3.6 which is still widely supported on Linux distros. This PE re-enables Python 3.6 support I also removed some upper bounds on deps to avoid some issues, as highlighted in https://iscinumpy.dev/post/bound-version-constraints/ Thanks for your kind consideration!

    opened by pombredanne 0
  • Ä° and Ä« not detected as mojibake

    Ä° and Ä« not detected as mojibake

    Hi @rspeer. Many thanks for creating and maintaining FTFY! We're using it at Sectigo to help prevent mojibake from finding its way into string fields in the digital certificates that we issue. We've noticed a couple of mojibake sequences that FTFY doesn't currently detect and fix:

    Desired behaviour:

    $ echo "Ä°stanbul" | iconv -t WINDOWS-1252
    İstanbul
    $ echo "RÄ«ga" | iconv -t WINDOWS-1252
    Rīga
    

    Current FTFY behaviour:

    $ echo "Ä°stanbul" | ftfy
    Ä°stanbul
    $ echo "RÄ«ga" | ftfy
    RÄ«ga
    

    Would it be possible to make FTFY handle these cases?

    opened by robstradling 0
  • On the wish list:

    On the wish list: "Pyreneeu00ebn" being explained as "Pyreneeën 71"

    A while ago I blogged about "Pyreneeën 71" on a web-site being incorrectly represented as "Pyreneeu00ebn".

    Basically the Unicode code point U+00EB : LATIN SMALL LETTER E WITH DIAERESIS is being represented as u00eb.

    Is this something that ftfy could potentially recognise?

    Right now It does not:

    >>> ftfy.fix_and_explain("Pyreneeu00ebn")
    ExplainedText(text='Pyreneeu00ebn', explanation=[])
    
    opened by jpluimers 2
  • Any idea which encoding failure could cause

    Any idea which encoding failure could cause "beëindiging" to be printed in a letter as "beï¿œindiging"?

    opened by jpluimers 0
Releases(v6.0.3)
  • v6.0.3(Aug 23, 2021)

    Updates in 6.0.x:

    • New function: ftfy.fix_and_explain() can describe all the transformations that happen when fixing a string. This is similar to what ftfy.fixes.fix_encoding_and_explain() did in previous versions, but it can fix more than the encoding.
    • fix_and_explain() and fix_encoding_and_explain() are now in the top-level ftfy module.
    • Changed the heuristic entirely. ftfy no longer needs to categorize every Unicode character, but only characters that are expected to appear in mojibake.
    • Because of the new heuristic, ftfy will no longer have to release a new version for every new version of Unicode. It should also run faster and use less RAM when imported.
    • The heuristic ftfy.badness.is_bad(text) can be used to determine whether there appears to be mojibake in a string. Some users were already using the old function sequence_weirdness() for that, but this one is actually designed for that purpose.
    • Instead of a pile of named keyword arguments, ftfy functions now take in a TextFixerConfig object. The keyword arguments still work, and become settings that override the defaults in TextFixerConfig.
    • Added support for UTF-8 mixups with Windows-1253 and Windows-1254.
    • Overhauled the documentation: https://ftfy.readthedocs.org
    • Requires Python 3.6 or later.
    Source code(tar.gz)
    Source code(zip)
  • v5.5.1(Mar 12, 2019)

Owner
Luminoso Technologies, Inc.
Luminoso Technologies, Inc.
Basic yet complete Machine Learning pipeline for NLP tasks

Basic yet complete Machine Learning pipeline for NLP tasks This repository accompanies the article on building basic yet complete ML pipelines for sol

Ivan 20 Aug 22, 2022
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Kashgari Overview | Performance | Installation | Documentation | Contributing 🎉 🎉 🎉 We released the 2.0.0 version with TF2 Support. 🎉 🎉 🎉 If you

Eliyar Eziz 2.3k Dec 29, 2022
TextFlint is a multilingual robustness evaluation platform for natural language processing tasks,

TextFlint is a multilingual robustness evaluation platform for natural language processing tasks, which unifies general text transformation, task-specific transformation, adversarial attack, sub-popu

TextFlint 587 Dec 20, 2022
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
AIDynamicTextReader - A simple dynamic text reader based on Artificial intelligence

AI Dynamic Text Reader: This is a simple dynamic text reader based on Artificial

Md. Rakibul Islam 1 Jan 18, 2022
The first online catalogue for Arabic NLP datasets.

Masader The first online catalogue for Arabic NLP datasets. This catalogue contains 200 datasets with more than 25 metadata annotations for each datas

ARBML 94 Dec 26, 2022
Codes for processing meeting summarization datasets AMI and ICSI.

Meeting Summarization Dataset Meeting plays an essential part in our daily life, which allows us to share information and collaborate with others. Wit

xcfeng 39 Dec 14, 2022
A text augmentation tool for named entity recognition.

neraug This python library helps you with augmenting text data for named entity recognition. Augmentation Example Reference from An Analysis of Simple

Hiroki Nakayama 48 Oct 11, 2022
Japanese NLP Library

Japanese NLP Library Back to Home Contents 1 Requirements 1.1 Links 1.2 Install 1.3 History 2 Libraries and Modules 2.1 Tokenize jTokenize.py 2.2 Cabo

Pulkit Kathuria 144 Dec 27, 2022
A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

A Deep Learning NLP/NLU library by Intel® AI Lab Overview | Models | Installation | Examples | Documentation | Tutorials | Contributing NLP Architect

Intel Labs 2.9k Jan 02, 2023
Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks for Sentence Classification Code for the paper Convolutional Neural Networks for Sentence Classification (EMNLP 2014). R

Yoon Kim 2k Jan 02, 2023
Simple Python script to scrape youtube channles of "Parity Technologies and Web3 Foundation" and translate them to well-known braille language or any language

Simple Python script to scrape youtube channles of "Parity Technologies and Web3 Foundation" and translate them to well-known braille language or any

Little Endian 1 Apr 28, 2022
voice2json is a collection of command-line tools for offline speech/intent recognition on Linux

Command-line tools for speech and intent recognition on Linux

Michael Hansen 988 Jan 04, 2023
Correctly generate plurals, ordinals, indefinite articles; convert numbers to words

NAME inflect.py - Correctly generate plurals, singular nouns, ordinals, indefinite articles; convert numbers to words. SYNOPSIS import inflect p = in

Jason R. Coombs 762 Dec 29, 2022
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

Yifan Wang 100 Dec 19, 2022
Incorporating KenLM language model with HuggingFace implementation of Wav2Vec2CTC Model using beam search decoding

Wav2Vec2CTC With KenLM Using KenLM ARPA language model with beam search to decode audio files and show the most probable transcription. Assuming you'v

farisalasmary 65 Sep 21, 2022
The official code for “DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction”, ACM MM, Oral Paper, 2021.

Good news! Our new work exhibits state-of-the-art performances on DocUNet benchmark dataset: DocScanner: Robust Document Image Rectification with Prog

Hao Feng 231 Dec 26, 2022
SGMC: Spectral Graph Matrix Completion

SGMC: Spectral Graph Matrix Completion Code for AAAI21 paper "Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning". Data Format

Chao Chen 8 Dec 12, 2022
Extracting Summary Knowledge Graphs from Long Documents

GraphSum This repo contains the data and code for the G2G model in the paper: Extracting Summary Knowledge Graphs from Long Documents. The other basel

Zeqiu (Ellen) Wu 10 Oct 21, 2022
NLP techniques such as named entity recognition, sentiment analysis, topic modeling, text classification with Python to predict sentiment and rating of drug from user reviews.

This file contains the following documents sumbited for Baruch CIS9665 group 9 fall 2021. 1. Dataset: drug_reviews.csv 2. python codes for text classi

Aarif Munwar Jahan 2 Jan 04, 2023