Show simple item record

dc.contributor.authorMandal, Ranju
dc.contributor.authorRoy, Partha Pratim
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2017-06-22T23:24:23Z
dc.date.available2017-06-22T23:24:23Z
dc.date.issued2015
dc.identifier.issn1520-5363
dc.identifier.doi10.1109/ICDAR.2015.7333885
dc.identifier.urihttp://hdl.handle.net/10072/340497
dc.description.abstractAutomatic document interpretation and retrieval is an important task to access handwritten digitized document repositories. In documents, the date is an important field and it has various applications such as date-wise document indexing/retrieval. In this paper a framework has been proposed for automatic date field extraction from handwritten documents. In order to design the system, sliding window-wise Local Gradient Histogram (LGH)-based features and a character-level Hidden Markov Model (HMM)-based approach have been applied for segmentation and recognition. Individual date components such as month-word (month written in word form i.e. January, Jan, etc.), numeral, punctuation and contraction categories are segmented and labelled from a text line. Next, a Histogram of Gradient (HoG)-based features and a Support Vector Machine (SVM)- based classifier have been used to improve the results obtained from the HMM-based recognition system. Subsequently, both numeric and semi-numeric regular expressions of date patterns have been considered for undertaking date pattern extraction in labelled components. The experiments are performed on an English document dataset and the encouraging results obtained from the approach indicate the effectiveness of the proposed system.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States of America
dc.relation.ispartofconferencename13th IAPR International Conference on Document Analysis and Recognition (ICDAR)
dc.relation.ispartofconferencetitle2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)
dc.relation.ispartofdatefrom2015-08-23
dc.relation.ispartofdateto2015-08-26
dc.relation.ispartoflocationNancy, FRANCE
dc.relation.ispartofpagefrom866
dc.relation.ispartofpagefrom5 pages
dc.relation.ispartofpageto870
dc.relation.ispartofpageto5 pages
dc.relation.ispartofedition1st
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleDate field extraction from handwritten documents using HMMs
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorMandal, Ranju


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record