Normal Template Mapping: An Association-Inspired Handwritten Character Recognition Model

In identifying objects, people usually associate memory templates to guide visual attention and determine the category of an object. The initial character images that children learn are usually normal patterns. However, the variation in corresponding handwritten patterns is quite large. To learn these deformed images with large variance, current deep models must involve millions of parameters for such kind of classification tasks that seem much easier and simpler to children who learn to recognize new characters associated with their initially taught normal patterns. From the perspective of humans’ perception, when people see a new object, they first think of a template image in their memory, which is similar to the object. This mapping process makes it easier for humans to learn new objects. Inspired by this cognitive association mechanism, this study developed a cognition-inspired handwritten character recognition model using a proposed normal template mapping neural network. This model uses an encoder-decoder architecture to build a normal template mapping neural network that transforms handwritten character images of one class to normalized characters similar to a given printed template character image representing that class. Then, a simple shallow classifier recognizes these normalized images, which are easier to classify. The experimental results show that the proposed model completes handwritten character recognition with comparable or higher precision at a much lower parameter count than current representative deep models. The proposed model removes the individual styles of handwritten character images and maps them to patterns similar to normal template images. This greatly reduces the classification difficulty and enables the classifier to classify only known standard character images.