Writer identification of Arabic handwriting documents using grapheme features

A system for Arabic writer identification using grapheme features and k-nearest neighbor classifier is built using Matlab programming language. The results of our preliminary study reveal that unknown writers can be identified by using edge base directional features and text-dependent method; however the simple system approach needs improvement to satisfy the requirements of real data. This project works on the following improvements: First a database of text- independent Arabic handwritten pages from around 100 different writers is gathered and used as a test bed. Then, features will be extracted from writers´ handwriting. Prior to feature extraction, preprocessing operations is applied to documents to remove the background. In this research, we build an interactive background removal interface. Then the multi-scale edge-hinge features and grapheme features will be extracted from the handwritten pages. The classification will be performed by a k-nearest neighbor classifier. The project studies the performance of the new features, and recognition operations on Arabic text, on the identification rate of writers. Matlab programming language is used to write the programs for this project. This project aims at building an Arabic writer identification system consisting of three main processes: an interactive preprocessing to remove documents background, a feature extraction process to extract feature vector, and a classification process. The three processes will be implemented on the training and testing phase of the system.