--- abstract: 'Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the NN. A major problem in handwriting recognition is the huge variability and distortions of patterns. Elastic models based on local observations and dynamic programming such HMM are not efficient to absorb this variability. But their vision is local. But they cannot face to length variability and they are very sensitive to distortions. Then the SVM is used to estimate global correlations and classify the pattern. Support Vector Machine (SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better recognition result. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - anshuman515@gmail.com creators_name: - family: anshu given: anshuman sharma honourific: Mr. lineage: '' date: 2012-03-20 date_type: submitted datestamp: 2012-11-09 19:35:25 department: ~ dir: disk0/00/00/81/18 edit_lock_since: ~ edit_lock_until: 0 edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 8118 fileinfo: text/html;http://cogprints.org/8118/2/anshu%20paper2.doc full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: unpub issn: ~ item_issues_comment: [] item_issues_count: ~ item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'Handwriting recognition, Support Vector Machine, Neural Network' lastmod: 2012-11-09 19:35:25 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: TRUE publication: no published publisher: ~ refereed: TRUE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 21 series: ~ source: ~ status_changed: 2012-11-09 19:35:25 subjects: - comp-sci-mach-learn succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: To Improve the Performance of Handwritten digit Recognition using Support Vector Machine type: journale userid: 16364 volume: ~