A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

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.