# A New Computational Method for Arabic Calligraphy Style Representation and Classification
###### Abstract  
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. document image analyzing; Arabic calligraphy; feature extraction; machine learning +
Footnote †: journal: Medical  
may be merged or connected even though they are not adjacent. Consequently, fonts do not offer flexibility compared to handwriting. Such flexibility makes the recognition of calligraphy or reproduction of text using similar calligraphy styles an extremely difficult task for a DA system. In this work, we aim to propose a tool that is able to classify Arabic calligraphy styles. Calligraphy style recognition (CSR) is considered an important research area for the following reasons: (1) It helps to read the text content. In other words, knowing the used style reflects the rules used in the writing of different characters, which in turn eases the reading task. (2) It helps to recognize the different parts of a document. Some styles are defined to represent specific parts in the document such as titles, footers, paragraphs, etc. (3) It also helps to grasp the history of a document. In paleography, CSR is used to define the era in which the document was written because styles appeared in different eras. (4) It also helps to define the origins (i.e., geographical area) in which a document was written. (5) It can be used for tutor purposes. Calligraphy learners use CSR systems to judge the quality of their writings in cases of experts' absence. Arabic is the fourth most spoken language in the world, and it is the first language of more than 200 million people across the world. Arabic script is the third most widely used script after Latin script [4]. Similar to other languages, it has grammar, spelling, punctuation rules, pronunciation, slang, and idioms. Several characteristics beyond the mere differences between languages make Arabic distinctive, including the number of variations and the written form. Arabic handwritten text is divided into two main parts: artistic writing called calligraphy and non-artistic (handwritten or printed) scripts. Arabic calligraphy (AC) is one of the most significant arts in the world. It is written only by hand. The first style of calligraphy was developed at the end of the 7 th century. Over time, the Muslim and Arab world developed many other styles of calligraphy. Due to the complex shape of the Arabic text, one must hold a certain level of expertise to be able to write AC texts or recognize a writing style. Before delving into Arabic calligraphy systems, we must shed some light on Arabic font recognition (AOFR) and how it differs from calligraphy recognition. On the one hand, the AOFR domain has received a lot of attention for several reasons: (a) the availability of public big datasets, (b) the ease of dealing with Arabic fonts due to their fixed shapes, (c) and the considerable amount of related works [5; 6] on the subject. On other hand, far less attention has been given to Arabic calligraphy style recognition (ACSR).