ERROR CONCEALMENT TECHNIQUES IN H.264/AVC FOR WIRELESS VIDEO TRANSMISSION IN MOBILE NETWORKS

Abstract

Recognition of handwritten characters of Indian script is difficult because of the presence of many complex shaped compound characters (cluster characters) as well as variability involved in the writing style of different individuals. This paper deals with a Modified Quadratic Discriminant Function (MQDF) based recognition technique for off-line Bangla handwritten compound characters. The features used for recognition purpose are mainly based on directional information obtained from the arc tangent of the gradient. To get the feature, at first, a 2 X 2 mean filtering is applied 4 times on the gray level image and a non-linear size normalization is done on the character image. The normalized image is then segmented to 49 x 49 blocks. A Roberts filter is then applied to obtain gradient image. Next, the arc tangent of the gradient (direction of gradient) is initially quantized into 32 directions and the strength of the gradient is accumulated with each of the quantized direction. These 32 directions are down sampled using Gaussian filter to get 8 directions, and 49 x 49 blocks are down sampled using a Gaussian filter into 7 x 7 blocks to get 392 (7 x 7 x 8) dimensional feature vector. Using 5- fold cross validation technique we obtained 85.90% accuracy from a dataset of Bangla compound characters containing 20,543 samples. Key words: Handwritten character recognition, Modified Quadratic Discriminant Function, Bangla Compound character, Indian script.

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