Data Cleansing and Preparation for Moving Toward Electronic Library Repository
Identifieur interne : 001320 ( Main/Exploration ); précédent : 001319; suivant : 001321Data Cleansing and Preparation for Moving Toward Electronic Library Repository
Auteurs : Asanee Kawtrakul [Thaïlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Abstract: Manually annotated metadata usually contains errors from mistyping; however, correcting those metadata manually could be costly and time consuming. This paper proposed a framework to ease metadata correction processed by proposing a system that utilizes OCR and NLP techniques to automatically extract metadata from document image. The system firstly converts images into text using OCR and then extracts metadata from OCR results. After that, the extracted metadata are compared with the data in existing repository to locate error entries. The error entries are then displayed to users whom will correct them using supporting information. Although human decision is required to correct the error manually, this step is necessary with only error entries. The experimental results with 3,712 thesis abstracts show that the proposed solution can automatically extract the relevance information with 91.41% accuracy.
Url:
DOI: 10.1007/11599517_69
Affiliations:
Links toward previous steps (curation, corpus...)
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- to stream PascalFrancis, to step Corpus: 000413
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- to stream Main, to step Merge: 001449
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: Manually annotated metadata usually contains errors from mistyping; however, correcting those metadata manually could be costly and time consuming. This paper proposed a framework to ease metadata correction processed by proposing a system that utilizes OCR and NLP techniques to automatically extract metadata from document image. The system firstly converts images into text using OCR and then extracts metadata from OCR results. After that, the extracted metadata are compared with the data in existing repository to locate error entries. The error entries are then displayed to users whom will correct them using supporting information. Although human decision is required to correct the error manually, this step is necessary with only error entries. The experimental results with 3,712 thesis abstracts show that the proposed solution can automatically extract the relevance information with 91.41% accuracy.</div>
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