Knowledge Extraction from Structured Engineering Drawings

As a typical type of structured documents, table drawings are widely used in engineering fields. Knowledge extraction of such structured documents plays an important role in automatic interpretation systems. In this paper, we propose a new knowledge extraction method based on automatically analyzing drawing layout and extracting physical or logical structures from the given engineering table drawings. Then based on the automatic interpretation results, we further propose normalization method to integrate varied types of engineering tables with other engineering drawings and extract implied domain knowledge.

[1]  Shijie Cai,et al.  A Vectorization System for Architecture Engineering Drawings , 2005, GREC.

[2]  Yalin Wang,et al.  Table structure understanding and its performance evaluation , 2004, Pattern Recognit..

[3]  Shijie Cai,et al.  An Object-Oriented Progressive-Simplification-Based Vectorization System for Engineering Drawings: Model, Algorithm, and Performance , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Thomas Kieninger,et al.  Three approaches to "industrial" table spotting , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[5]  Tong Lu,et al.  Automatic analysis and integration of architectural drawings , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[6]  Richard Zanibbi,et al.  A survey of table recognition: Models , 2004 .

[7]  Daniel P. Lopresti,et al.  Medium-independent table detection , 1999, Electronic Imaging.

[8]  Tong Lu,et al.  A new recognition model for electronic architectural drawings , 2005, Comput. Aided Des..

[9]  J. Cordy,et al.  A Survey of Table Recognition : Models , Observations , Transformations , and Inferences , 2003 .

[10]  Tong Lu,et al.  3D Reconstruction of Detailed Buildings from Architectural Drawings , 2005 .

[11]  Konstantin Zuyev.  Table image segmentation , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.