Please use this identifier to cite or link to this item:
https://doi.org/10.1023/A:1023245904128
DC Field | Value | |
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dc.title | Text retrieval from document images based on word shape analysis | |
dc.contributor.author | Tan, C.L. | |
dc.contributor.author | Huang, W. | |
dc.contributor.author | Sung, S.Y. | |
dc.contributor.author | Yu, Z. | |
dc.contributor.author | Xu, Y. | |
dc.date.accessioned | 2013-07-04T07:40:51Z | |
dc.date.available | 2013-07-04T07:40:51Z | |
dc.date.issued | 2003 | |
dc.identifier.citation | Tan, C.L., Huang, W., Sung, S.Y., Yu, Z., Xu, Y. (2003). Text retrieval from document images based on word shape analysis. Applied Intelligence 18 (3) : 257-270. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1023245904128 | |
dc.identifier.issn | 0924669X | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/39403 | |
dc.description.abstract | In this paper, we propose a method of text retrieval from document images using a similarity measure based on word shape analysis. We directly extract image features instead of using optical character recognition. Document images are segmented into word units and then features called vertical bar patterns are extracted from these word units through local extrema points detection. All vertical bar patterns are used to build document vectors. Lastly, we obtain the pair-wise similarity of document images by means of the scalar product of the document vectors. Four corpora of news articles were used to test the validity of our method. During the test, the similarity of document images using this method was compared with the result of ASCII version of those documents based on the N-gram algorithm for text documents. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1023/A:1023245904128 | |
dc.source | Scopus | |
dc.subject | Document image analysis | |
dc.subject | Document vector | |
dc.subject | Similarity measure | |
dc.subject | Text retrieval | |
dc.type | Article | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.doi | 10.1023/A:1023245904128 | |
dc.description.sourcetitle | Applied Intelligence | |
dc.description.volume | 18 | |
dc.description.issue | 3 | |
dc.description.page | 257-270 | |
dc.description.coden | APITE | |
dc.identifier.isiut | 000182096600003 | |
Appears in Collections: | Staff Publications |
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