TY  - EJOU
AU  - Saleem,  Ibraheem 
AU  - Abdulazeez, Adnan Mohsin 
AU  - Orman, Zeynep 

TI  - A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 68
IS  - 2
SN  - 1546-2226

AB  - The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In this study, we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks. This modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary images. Also, propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its background. Image projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary images. The training stage involves taking a number of images for model training. These images are selected randomly with different angles to generate four classes (0–90, 90–180, 180–270, and 270–360). The proposed segmentation approach achieves a high accuracy of 98.18%. The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of importance. The proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.
KW  - Writer identification; handwritten Arabic; biometric systems; artificial neural network; segmentation; skew detection model

DO  - 10.32604/cmc.2021.016447