ARBML

Motivation

As you know machine learning has proven its importance in many fields, like computer vision, NLP, reinforcement learning, adversarial learning, etc .. Unfortunately, there is a little work to make machine learning accessible for Arabic-speaking people.

Goal

Our goal is to enrich the Arabic content by creating open-source projects and open the community eyes on the significance of machine learning. We want to create interactive applications that allow novice Arabs to learn more about machine learning and appreciate its advances.

Challenges

Arabic language has many complicated features compared to other languages. First, Arabic language is written right to left. Second, it contains many letters that cannot be pronounced by most foreigners like ض ، غ ، ح ، خ، ظ. Moreover, Arabic language contains special characters called Diacritics which are special characters that help readers pronounced words correctly. For instance the statement ` السَّلامُ عَلَيْكُمْ وَرَحْمَةُ اللَّهِ وَبَرَكَاتُهُ containts special characters after most of the letters. The diactrics follow special rules to be given to a certain character. These rules are construct a complete area called النَّحْوُ الْعَرَبِيُّ. Compared to English, the Arabic language words letters are mostly connected اللغة as making them disconnected is difficult to read ا ل ل غ ة`. Finally, there as many as half a billion people speaking Arabic which resulted in many dialects in different countires.

Procedure

Our procedure is generalized and can be generalized to many language models not just Arabic. This standrized approach takes part as multiple steps starting from training on colab then porting the models to the web.

Models

</td> </td> </td> </td>

Name

Description

Notebook

Demo

Arabic Diacritization

Simple RNN model ported from Shakkala

Arabic2English Translation

seq2seq with Attention

Arabic Poem Generation

CharRNN model with multinomial distribution

Arabic Words Embedding

N-Grams model ported from Aravec

Arabic Sentiment Classification

RNN with Bidirectional layer

Arabic Image Captioning

Encoder-Decoder architecture with attention

Arabic Word Similarity

Embedding layers using cosine similarity

Arabic Digits Classification

Basic RNN model with classification head

Arabic Speech Recognition

Basic signal processing and classification

Arabic Object Detection

SSD Object detection model

</td> </tr>

Arabic Poems Meter Classification

Bidirectional GRU

</td> </tr>

Arabic Font Classification

CNN

</td> </tr>

Arabic Text Detection

Optical Character Recognition (OCR)

</td> </tr> </table> ## Datasets

Name	Description
Arabic Digits	70,000 images (28x28) converted to binary from Digits
Arabic Letters	16,759 images (32x32) converted to binary from Letters
Arabic Poems	146,604 poems scrapped from aldiwan
Arabic Translation	100,000 paralled arabic to english translation ported from OpenSubtitles
Product Reviews	1,648 reviews on products ported from Large Arabic Resources For Sentiment Analysis
Image Captions	30,000 Image paths with captions extracted and translated from COCO 2014
Arabic Wiki	4,670,509 words cleaned and processed from Wikipedia Monolingual Corpora
Arabic Poem Meters	55,440 verses with their associated meters collected from aldiwan
Arabic Fonts	516 100×100 images for two classes.