Invariant Recognition of Hand-Drawn Pictograms Using HMMs with a Rotating Feature Extraction

In this paper, we present a new feature extraction technique and a novel Hidden Markov Model (HMM) based classifier for the rotation, translation and scale invariant recognition of handdrawn pictograms. The feature extraction is performed by taking a fixed dimensional vector along the radius of a circle surrounding the pictogram. Within the HMM-framework these features are used to classify the pictogram and to estimate the rotation angle of the pattern using the segmentation power of the Markov Models. Three variations of the classifier design are presented, giving the option to choose between recognition with preferred rotation angles and fully rotation invariant recognition. The proposed techniques show high recognition rates up to 99.5% on two large pictogram databases consisting of 20 classes, where significant shape variations occur within each class due to differences in how each element is drawn. In order to obtain a detailed evaluation of our methods, experimental results for conventional approaches utilizing moments and neural nets are given in comparison. The techniques can be easily adapted to handle grey scale or colour images and we demonstrate this by showing some results of our experimental image retrieval by user sketch system which serves also as an example for future applications.