Please use this identifier to cite or link to this item:
http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3764
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rajput G.G | |
dc.contributor.author | Ummapure S.B. | |
dc.date.accessioned | 2020-06-12T15:01:15Z | - |
dc.date.available | 2020-06-12T15:01:15Z | - |
dc.date.issued | 2018 | |
dc.identifier.citation | IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017 , Vol. , , p. 520 - 526 | en_US |
dc.identifier.uri | 10.1109/ICPCSI.2017.8392348 | |
dc.identifier.uri | http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3764 | - |
dc.description.abstract | Automatic identification of scripts from document images helps selecting appropriate OCR for character recognition and content retrieval. In this paper, Scale invariant Feature Transformation (SIFT) based script identification has been proposed. Features are extracted using SIFT approach at word level (two, three or more character words) and KNN classifier has been used to recognize the script. Experiments are performed by extracting the words from document images consisting of English, Kannada, and Devanagari scripts. Overall accuracy reported for the proposed system is 97.65% and 96.71% for bi-script and tri-scripts, respectively. © 2017 IEEE. | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.subject | bi-script | |
dc.subject | Document image | |
dc.subject | KNN | |
dc.subject | Scale invariant | |
dc.subject | Script recognition | |
dc.subject | SIFT | |
dc.title | Script identification from handwritten documents using SIFT method | en_US |
dc.type | Conference Paper | |
Appears in Collections: | 2. Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.