Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition

Subspace Classifiers in Recognition of Handwritten Digits

This thesis consists of two parts. The first part reviews the general structure of a pattern recognition system and, in particular, various statistical and neural classification algorithms. The presentation then focuses on subspace classification methods that form a family of semiparametric methods. Several improvements on the traditional subspace classification rule are presented. Most importa...

Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...

Multiple Feature Extraction and Hierarchical Classifiers for Emotions Recognition

The recognition of the emotional states of speaker is a multidisciplinary research area that has received great interest in the last years. One of the most important goals is to improve the voiced-based human-machine interactions. Recent works on this domain use the prosodic features and the spectrum characteristics of speech signal, with standard classifier methods. Furthermore, for traditiona...

Handwritten Digits Recognition

Recognition of handwritten digits using sparse codes generated by local feature extraction methods

We investigate when sparse coding of sensory inputs can improve performance in a classification task. For this purpose, we use a standard data set, the MNIST database of handwritten digits. We systematically study combinations of sparse coding methods and neural classifiers in a two-layer network. We find that processing the image data into a sparse code can indeed improve the classification pe...