Recognizing Trac Signs using Flexible Discrete Cosine Transform (DCT) Grid

Trac sign recognition can be performed in two phases i.e. (1) detection and (2) recognition; detection deals with sensing a trac sign in real world image or video frame while recognition is aboutreading its contents. A trac signs database may contain samples with varying font sizes and stylesused for printing the interior of a trac sign and the contents may also be shifted away from the center of gravity. In this paper, we utilize the energy compaction property of Discrete Cosine Transform (DCT) to propose a trac sign recognition (TSR) system which can generate invariant features for varying font styles, scaled up, scaled down and translated contents of a sign. Experiments on synthetic and real world images datasets show that the features generated by our proposed method have great intraclass similarity and interclass variation. We have also shown that our proposed method outperforms Eigen based recognition method [1] and is comparable with the histogram of oriented gradient (HOG) approach [2] using support vector machine (SVM) classi er.