Earticle

As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.

Writing in its different forms, printed and manuscript has always been a tool essential in human communication, and as ubiquitous in most areas of its operations. It is used to store and archive knowledge. Thereby, human has always developed techniques for sustainability across generations. Indeed, with the advent of new information technologies and electronics computers, and further increase the power of machines, automated processing. Besides, one of the important system is the handwriting recognition. Handwriting text recognition system based on efficient segmentation, DCT and HOG Features is proposed. The proposed system depends on the segmentation of the text into words only. Moreover, the system achieved best recognition accuracy 96.317% based on the used methods and SVM classifier.