Object recognition using Hausdorff distance for multimedia applications

The need for reliable and efficient systems for recognition of object from image is increasing day by day. A partial list of applications that may use such system includes searching and reading in hand written documents, recognizing digit on papers and others. In the existing work, Euclidean distance is used for recognizing object, but some of object it doesn’t work well. The major aim of the work is to introduce new object recognition. So the proposed work recognizing object using a shape context and Hausdorff distance is introduced. The process analyses the layout of the image into digits. In the first step, the shape context is computed for two point set and Hungarian algorithm is used to find the correspondence between two point set. The process evaluates the similarity of the two point set using Hausdorff distance. Finally, the error rate is calculated by considering the affine cost and shape context cost. The algorithm tested using the MNIST, COIL data sets and a private collection of hand written digits and encouraging results were obtained. The error rate is reduced to 0.72%.