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Features Extraction of Arabic Calligraphy using extended Triangle Model for Digital Jawi Paleography Analysis

The style of writing or calligraphy applied in ancient 
manuscripts gives useful information to paleographers. The 
information helps paleographer to identify date, writer, number 
of writers, place of origin, and the originality of manuscripts. 
This information is known as features. The features from 
characters, tangent value, dominant background and also 
Grey-Level Co-occurrence Matrix (GLCM) have been used in 
this field of research. A novel technique was proposed for digital 
Jawi Paleography. Jawi is the original Malay writing based on 
Arabic characters. The technique proposed models triangles on 
images and extracts features from them. The features are used 
for classification. In this paper, new features for the Triangle 
Model are proposed. Also, the implementation of four zones is 
reported. The number of features has been extended from 12 to 
45. For validation of proposed algorithm, 60,000:20,000 training 
and testing data from Hoda digit dataset has been prepared, 
selected randomly for 10 rounds of testing. For further 
verification, two Supervised Machine Learning (SML) and three 
Unsupervised Machine Learning (UML) algorithms were 
experimented. These experiments were conducted using a new 
Arabic calligraphy dataset that was set up from 1,225 Arabic 
letters taken from calligraphy books. From the data, SML 
experiments were conducted with the ratio of 807:408 for 
training and testing. Whereas for the UML, three classifiers 
were tested on 30 images of Arabic calligraphy dataset. Results 
from the tests prove that the Triangle Model technique can 
successfully be used in digital paleography of Jawi characters.