In this paper, we present a new approach for hand-written character and digit recognitions based on shape descriptor and the Hausdorff Context. We start at finding the corresponding points between two shapes by using a modified shape context. We then use these correspondences as key geometric points for shape alignment with the Thin Plate Spline (TPS) model. After the transformation has been applied completely, the distance between two shapes is computed by a new distance measure, the Hausdorff Context. We achieve a very high recognition rate of 98.7% on 268 images. Recognizing of an object in an unconstrained condition is a hard problem. One of the examples of the distorted object recognition is the hand written character/digit recog- nition. In order to handle the situations we may deal with during test phase, a robust method that is insensitive to variations of an image should be constructed. This includes effects due to varying in writing styles and sizes of characters. It is known that the same characters written by different users may be seen as different classes of the object due to the local distortion, size variation and rotation of character. Fu et al. (4) proposed an adaptive learning system for hand written Chinese character recognition. They mainly addressed the user adaption issues using self-growing probabilistic decision-based neu- ral networks (SPDNNs). The user adaptation of the parameters of SPDNN was for- mulated as incremental reinforced and antireinforced learning procedures. In summary, they have developed 1) an SPDNN based handwriting recognition system; 2) a two- stage recognition structure; and 3) a three-phase training methodology for a) a global coarse classifier; b) a user independent hand written character recognizer; and c) a user adaptation module on a personal computer. The SPDNN has some nice properties be- cause of its discrimination function obeys a probability constraint so that a lower false acceptance and false rejection rates can be achieved. Torres-Mendezet al. (5) presented a new method for object recognition under translation, rotation and scaling conditions that has been applied to character recognition. In the preprocessing step, they take into account the invariant properties of the normalized moment of inertia. They also pro- posed a new coding of an object which describes its topological characteristics. The vectors obtained in the preprocessing step were then used as inputs to a holographic nearest-neighbor (HNN), a fast algorithm for object recognition. The algorithm was
['Sanun Srisuk', 'Marut Tamsri', 'Rerkchai Fooprateepsiri', 'P. Sookavatana', 'Khamron Sunat']
A New Shape Matching Measure for Nonlinear Distorted Object Recognition