Arabic online word extraction from handwritten text using SVM-RBF classifiers decision fusion

• Authors: Randa Ibrahim Elanwar;Mohsen Rashwan;Samia Mashali
• Venue: EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
• Year: 2012

In this paper, we propose a system for Arabic online word extraction from handwritten text lines, a problem addressed for the first time for Arabic language as there is no public dataset of Arabic online handwritten texts available so far. We collected a dataset of unconstrained online handwritten sentences and used it to design and evaluate our system. First, our system classifies the white gaps between words connected components into either intra-word or interword gap according to some local and global online features extracted from each gap together with the groups of strokes encompassing the gap. The classifier is a polynomial kernel support vector machine (SVM) which decisions are used for initial word extraction. A post stage is added to the system to test the extracted words for under-segmentation and resolve this undersegmentation by reconsidering the gap type decisions for the stuck word. Classifiers decision fusion takes place by consulting five different classifiers (four SVM and a radial basis function neural network 'RBF NN') and feeding their decisions to a separate pre-trained SVM to make the final decision. Most stuck words are correctly detected and a lot of them have been correctly resolved. The post stage leads to remarkable error reduction compared to single classifiers performance. Promising results are achieved regarding the fact that the unconstrained Arabic handwriting nature adds more difficulties to the problem.