Digit Recognition Based on Specialization, Decomposition and Holistic Processing

With the introduction of the Convolutional Neural Network (CNN) and other classical algorithms, facial and object recognition have made significant progress. However, in a situation where there are few label examples or the environment is not ideal, such as lighting conditions, orientations, and so on, performance is disappointing. Various methods, such as data augmentation and image registration, have been used in an effort to improve accuracy; nonetheless, performance remains far from human efficiency. Advancement in cognitive science has provided us with valuable insight into how humans achieve high accuracy in identifying and discriminating between different faces and objects. These researches help us understand how the brain uses the features in the face to form a holistic representation and subsequently uses it to discriminate between faces. Our objective and contribution in this paper is to introduce a computational model that leverages these techniques, being used by our brain, to improve robustness and recognition accuracy. The hypothesis is that the biological model, our brain, achieves such high efficiency in face recognition because it is using a two-step process. We therefore postulate that, in the case of a handwritten digit, it will be easier for a learning model to learn invariant features and to generate a holistic representation than to perform classification. The model uses a variational autoencoder to generate holistic representation of handwritten digits and a Neural Network(NN) to classify them. The results obtained in this research show the effectiveness of decomposing the recognition tasks into two specialize sub-tasks, a generator, and a classifier.