HANDWRITTEN DIGIT CLASSIFICATION USING DEEP LEARNING

Abstract

Abstract Representation of data is identified as a very important concept before applying any classification technique as it helps to make sense of data (images, videos etc.) and learn features. However training a single layer linear or non linear classifier has serious limitations considering the vastness of variability in data. The variability can be expressed in terms of handwriting of a person, pre-processing of images in the problem domain of classifying handwritten digits. Selection of features/latent factors therefore becomes an important aspect of classification since they are able to represent more abstract concepts related to data and each one can be provided a unique significance value. We have compared various approaches and their variations to generate an optima set of features which can be used for the classification problem of handwritten digits. Restricted Boltzmann machines(RBM) which form the baseline for deep learning are used to discover latent factors which then feed forward to higher level RBM’s or classifiers. The classifiers studied in the research include Linear Mapping, Radial Basis Function Neural Network, and Backpropagation and up-down algorithm. Results from all variations in RBM parameters and classifiers are observed and discussed. We have compared our results with other related works and it is found that the maximum accuracy achieved is 97.7%