Deep Arabic Font Family and Font Size Recognition

Despite the importance of recognizing Arabic calligraphy styles and their potential usefulness for many applications, a very limited number of Arabic calligraphy style recognition works have been established. Thus, we propose a new computational tool for Arabic calligraphy style recognition (ACSR). The present work aims to identify Arabic calligraphy style (ACS) from images where text images are captured by different tools from different resources. To this end, we were inspired by the indices used by human experts to distinguish different calligraphy styles. These indices were transformed into a descriptor that defines, for each calligraphy style, a set of specific features. Three scenarios have been considered in the experimental part to prove the effectiveness of the proposed tool. The results confirmed the outperformance of both individual and combine features coded by our descriptor. The proposed work demonstrated outstanding performance, even with few training samples, compared to other related works for Arabic calligraphy recognition.

show abstract

Character-independent font identification

Haraguchi¹,

Harada²,

Iwana³

et al. 2020

Preprint

View full text Add to dashboard Cite

There are a countless number of fonts with various shapes and styles. In addition, there are many fonts that only have subtle differences in features. Due to this, font identification is a difficult task. In this paper, we propose a method of determining if any two characters are from the same font or not. This is difficult due to the difference between fonts typically being smaller than the difference between alphabet classes. Additionally, the proposed method can be used with fonts regardless of whether they exist in the training or not. In order to accomplish this, we use a Convolutional Neural Network (CNN) trained with various font image pairs. In the experiment, the network is trained on image pairs of various fonts. We then evaluate the model on a different set of fonts that are unseen by the network. The evaluation is performed with an accuracy of 92.27%. Moreover, we analyzed the relationship between character classes and font identification accuracy.

show abstract

Deep Arabic Font Family and Font Size Recognition

Cited by 3 publications

References 6 publications

Exploring multi-tasking learning in document attribute classification

Exploring multi-tasking learning in document attribute classification

A New Computational Method for Arabic Calligraphy Style Representation and Classification

Character-independent font identification

Contact Info

Product

Resources

About