Automatic reading of cursive scripts using human knowledge

This paper presents a model for reading cursive script which has an architecture inspired by a reading model and which is based on perceptual concepts. We limit the scope of our study to the off-line recognition of isolated cursive words. First of all, we justify why we chose McClelland & Rumelhart's reading model as the inspiration for our system. A brief resume of the method's behavior is presented, and the main originalities of our model are underlined. After this, we focus on the new updates added to the original system: a new baseline extraction module, a new feature extraction module, and a new generation, validation and hypothesis insertion process. After implementation of our method, new results have been obtained on real images from a training set of 184 images, and a testing set of 100 images, and are discussed. We are concentrating now on validating the model using a larger database.