AraDIC: Arabic Document Classification Using Image-Based Character Embeddings and Class-Balanced Loss

Abstract

Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering. These could be eliminated by using character-level features. We propose a novel end-to-end Arabic document classification framework, Arabic document imagebased classifier (AraDIC), inspired by the work on image-based character embeddings. AraDIC consists of an image-based character encoder and a classifier. They are trained in an end-to-end fashion using the class balanced loss to deal with the long-tailed data distribution problem. To evaluate the effectiveness of AraDIC, we created and published two datasets, the Arabic Wikipedia title (AWT) dataset and the Arabic poetry (AraP) dataset. To the best of our knowledge, this is the first image-based character embedding framework addressing the problem of Arabic text classification. We also present the first deep learningbased text classifier widely evaluated on modern standard Arabic, colloquial Arabic and classical Arabic. AraDIC shows performance improvement over classical and deep learning baselines by 12.29% and 23.05% for the micro and macro F-score, respectively.

A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation

“…The most important technique for Arabic text classification is usually representation and classification, so in this section, we will survey the most important steps for that reason. In this section, we will conduct a brief literature review focusing on two key stages: representation such as paper [6,7] and classification such as paper [8] as follows:…”

“…In 2020, Daif et al presented AraDIC [6], the first deep learning framework for Arabic document classification based on image-based characters Ameur et al suggested a hybrid CNN and RNN deep learning model for categorizing Arabic text documents using static, dynamic, and fine-tuned word embedding [3]. The most meaningful representations from the space of Arabic word embedding are automatically learned using a deep learning CNN model.…”