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ÇѱÛÁ¦¸ñ(Korean Title) |
½ÉÃþ ½Å°æ¸ÁÀ» È°¿ëÇÑ ÀüÀÚ¹®¼ ³» °´Ã¼ÀÇ ÀÚµ¿ ÃßÃâ ¹æ¹ý ¿¬±¸ |
¿µ¹®Á¦¸ñ(English Title) |
Automatic Object Extraction from Electronic Documents Using Deep Neural Network |
ÀúÀÚ(Author) |
ÀåÈñÁø
俵ÈÆ
ÀÌ»ó¿ø
Á¶Áø¿ë
Heejin Jang
Yeonghun Chae
Sangwon Lee
Jinyong Jo
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¿ø¹®¼ö·Ïó(Citation) |
VOL 07 NO. 11 PP. 0411 ~ 0418 (2018. 11) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
With the proliferation of artificial intelligence technology, it is becoming important to obtain, store, and utilize scientific data in research and science sectors. A number of methods for extracting meaningful objects such as graphs and tables from research articles have been proposed to eventually obtain scientific data. Existing extraction methods using heuristic approaches are hardly applicable to electronic documents having heterogeneous manuscript formats because they are designed to work properly for some targeted manuscripts. This paper proposes a prototype of an object extraction system which exploits a recent deep-learning technology so as to overcome the inflexibility of the heuristic approaches. We implemented our trained model, based on the Faster R-CNN algorithm, using the Google TensorFlow Object Detection API and also composed an annotated data set from 100 research articles for training and evaluation. Finally, a performance evaluation shows that the proposed system outperforms a comparator adopting heuristic approaches by 5.2%.
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Å°¿öµå(Keyword) |
°´Ã¼ ÃßÃâ
½ÉÃþ ÇнÀ
ÅÙ¼Ç÷οì
ÀüÀÚ¹®¼
Object Extraction
Deep Learning
Tensorflow
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