Isolated Handwritten Digit Recognition Using oBIFs and Background Features

This study demonstrates how the combination of oriented Basic Image Features (oBIFs) with the background concavity features can be effectively employed to enhance the performance of isolated digit recognition systems. The features are extracted without any size normalization from the complete image as well as from different regions of the image by applying a uniform grid sampling to the image. Classification is carried out using one-against-all support vector machine (SVM) while the experimental study is conducted on the standard CVL single digit database. A series of evaluations using different feature configurations and combinations realized high recognition rates which are compared with the state-of-the-art methods on this subject.