Data Cleansing and Preparation for Moving Toward Electronic Library Repository

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.

Authors and Affiliations

1. Department of Computer Engineering, Kasetsart University, Bangkok, 10900, Thailand

   Asanee Kawtrakul

Editors and Affiliations

1. Virginia Tech, 24061, Blacksburg, VA

   Edward A. Fox

2. University of Vienna, Vienna, Austria

   Erich J. Neuhold

3. Department of Library Science, Chulalongkorn University, 10330, Bangkok, Thailand

   Pimrumpai Premsmit

4. School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, 12120, Klong Luang, Pathum Thani, Thailand

   Vilas Wuwongse

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