Urdu to Braille Translation for Blind People.

Braille is a tactile writing system used by visually impaired people.
It can be read on embossed paper or using refreshable braille displays connecting to computers and smartphone devices.
Braille can be written using a slate and stylus, a braille writer, an electronic braille notetaker, or a computer connected to a braille embosser.

Liblouis is an open-source braille translator and back-translator named in honor of Louis Braille. It features support for computer and literary braille, supports contracted and uncontracted translation for many languages, and has support for hyphenation. New languages can easily be added through tables that support a rule- or dictionary-based approach. Tools for testing and debugging tables are also included.
Install louis on your OS.

git clone https://github.com/liblouis/liblouis
cd liblouis
./configure
make 
sudo make install
sudo ldconfig

To use it in Python you need to install it for Python 3

cd python
sudo python3 setup.py install

Now you can use it on the command line or in Python.

import louis

table = ["ur-pk-g2.ctb"]

line = "ایک قدم آگے بڑھیں۔"
translation = louis.translateString(table, line, 0, 0)

print(translation)

and the translation for the sentence is

⠁ ⠟⠙⠍ ⠜⠛⠌ ⠃⠻⠦⠊⠰⠲

You can use the backtranslation as well.

print(louis.backTranslateString(table, translation))

and the output of back translation is.

ایک قدم آگے بڑھیں۔

Comments

Text Summarization for Urdu: Part 1

Text Summarization is an important task for large documents to get the idea of the document. There are two main summarization techniques used in NLP for text summarization. Extractive Text Summarization : This approach's name is self-explanatory. Most important sentences or phrases are extracted from the original text and a short summary provided with these important sentences. See the figure for the explanation. Abstractive Text Summarization : This approach uses more advanced deep learning techniques to generate new sentences by learning from the original text. It is a complex task and requires heavy computing power such as GPU. Let's dive into the code for generating the text summary. I'm using Arabic as a parameter because the contributor did an excellent job of handling a lot of things like stemming, Urdu characters support, etc. from summa.summarizer import summarize text = """ اسلام آباد : صدر مملکت ڈاکٹر عارف علوی بھی کورونا وائرس کا شکار ہوگئے۔ سما...

Urdu Tokenization using SpaCy

SpaCy is an NLP library which supports many languages. It’s fast and has DNNs build in for performing many NLP tasks such as POS and NER. It has extensive support and good documentation. It is fast and provides GPU support and can be integrated with Tensorflow, PyTorch, Scikit-Learn, etc. SpaCy provides the easiest way to add any language support. A new language can be added by simply following Adding Languages article. I’ve added the Urdu language with dictionary-based lemmatization, lexical support and stop words( Urdu ). Here is how you can use the tokenizer for the Urdu language. First, install SpaCy . $ pip install spacy Now import spacy and create a blank object with support of Urdu language. I’m using blank because there is no proper model available for Urdu yet, but tokenization support available. import spacy nlp = spacy.blank('ur') doc = nlp(" کچھ ممالک ایسے بھی ہیں جہاں اس برس روزے کا دورانیہ 20 گھنٹے تک ہے۔") print("Urdu Tokeniza...

Transformer Based QA System for Urdu

Question Answer Bot The Question-Answer System is the latest trend in NLP. There are currently two main techniques used for the Question-Answer system. 1 - Open Domain: It is a wast land of NLP applications to build a QA system. A huge amount of data and text used to build such a system. I will write a blog post later about using the Open-Domain QA system. 2 - Closed Domain: A closed domain question system is a narrow domain and strictly answers the questions which can be found in the domain. One example of a Closed Domain question system is a Knowledge-Based system. In this tutorial, I will explain the steps to build a Knowledge-Based QA system. Knowledge Base (KB) question answers are mostly used for FAQs. Where the user asks the questions and the model returns the best-matched answer based on the question. It's easy to implement and easy to integrate with chatbots and websites. It is better to use the KB system for small datasets or narrow domains like...

UrduNLP

Search This Blog