Subspace classifiers in recognition of handwritten digits

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Laaksonen, Jorma
dc.date.accessioned 2012-02-10T09:12:47Z
dc.date.available 2012-02-10T09:12:47Z
dc.date.issued 1997-05-07
dc.identifier.isbn 951-22-5479-4
dc.identifier.issn 1238-9803
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2156
dc.description.abstract 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 importantly, two new classification techniques, here named the Local Subspace Classifier (LSC) and the Convex Local Subspace Classifier (LSC+), are introduced. These new methods connect the subspace principle to the family of nonparametric prototype-based classifiers and, thus, seek to combine the benefits of both approaches. The second part addresses the recognition of handwritten digits, which is the case study of this thesis. Special attention is given to feature extraction methods in optical character recognition systems. As a novel contribution, a new method, here named the error-corrective feature extraction, is presented. The prototype recognition system developed for the experiments is described and various options in the implementation are discussed. For the background of the experiments, thirteen well-known statistical and neural classification algorithms were tested. The results obtained with two traditional subspace methods and ten novel techniques presented in this thesis are compared with them. The results show that the Convex Local Subspace Classifier performs better than any other classification algorithm in the comparison. The conclusions of this thesis state that the suggested enhancements make the subspace methods very useful for tasks like the recognition of handwritten digits. This result is expected to be applicable in other similar cases of recognizing two-dimensional isolated visual objects. en
dc.format.extent 152
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Acta polytechnica Scandinavica. Ma, Mathematics, computing and management in engineering series en
dc.relation.ispartofseries 84 en
dc.subject.other Computer science en
dc.title Subspace classifiers in recognition of handwritten digits en
dc.type G4 Monografiaväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Computer Science and Engineering en
dc.contributor.department Tietotekniikan osasto fi
dc.subject.keyword pattern recognition en
dc.subject.keyword adaptive systems en
dc.subject.keyword neural networks en
dc.subject.keyword statistical classification en
dc.subject.keyword subspace methods en
dc.subject.keyword prototype-based classification en
dc.subject.keyword feature extraction en
dc.subject.keyword optical character recognition en
dc.subject.keyword handwritten digits en
dc.subject.keyword classifier comparison en
dc.subject.keyword benchmarking study en
dc.identifier.urn urn:nbn:fi:tkk-001249
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (monografia) fi
dc.type.ontasot Doctoral dissertation (monograph) en
dc.contributor.lab Laboratory of Computer and Information Science en
dc.contributor.lab Informaatiotekniikan laboratorio fi


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse

My Account