Segmentation of Arabic text is a major challenge that shall be addressed by any recognition system. The cursive nature of Arabic writing makes it necessary to handle the segmentation issue at various levels. Arabic text line can be viewed as a sequence of words which in turn can be viewed as a sequence of sub-words. Sub-words have the frequently encountered intrinsic property of sharing the same vertical space which makes vertical projection based segmentation technique inefficient. In this paper, the task of segmenting handwritten Arabic text at sub-word level is taken up. The proposed algorithm is based on pulling away the connected components to overcome the impossibility of separating them by vertical projection based approach. Graph theoretic modeling is proposed to solve the problem of connected component extraction. In the sequel, these components are subjected to thorough analysis in order to obtain the constituent sub-words where a sub-word may consist of many components. The proposed algorithm was tested using variety of handwritten Arabic samples taken from different databases and the results obtained are encouraging. 

Information hiding in digital media such as audio, video and or images in order to establish the owner rights and to protect the copyrights commonly known as digital watermarking has received considerable attention of researchers over last few decades and lot of work has been done accordingly. A number of schemes and algorithms have been proposed and implemented using different techniques. The effectiveness of the technique depends on the host data values chosen for information hiding and the way watermark is being embedded in them. However, in view of the threats posed by the online pirates, the robustness and the security of the underlying watermarking techniques have always been a major concern of the researchers. This paper presents a secure and robust watermarking technique for color images using Discrete Wavelet Transformation. The results obtained have shown that the technique is robust against various common image processing attacks.