摘要
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.
