Issues in Ground-Truthing Graphic Documents
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
A Theoretical Foundation and a Method for Document Table Structure Extraction and Decompositon
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Exploiting WWW Resources in Experimental Document Analysis Research
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Table Detection via Probability Optimization
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
TableSeer: automatic table metadata extraction and searching in digital libraries
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Generating Ground Truthed Dataset of Chart Images: Automatic or Semi-automatic?
Graphics Recognition. Recent Advances and New Opportunities
Identifying table boundaries in digital documents via sparse line detection
Proceedings of the 17th ACM conference on Information and knowledge management
Table detection in heterogeneous documents
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
An open approach towards the benchmarking of table structure recognition systems
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
An efficient pre-processing method to identify logical components from PDF documents
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Semi-automatic ground truth generation for chart image recognition
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Table detection in document images using header and trailer patterns
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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Abstract: In this paper, we first describe an automatic table ground truth generation system which can efficiently generate a large amount of accurate table ground truth suitable for the development of table detection algorithms. Then a novel background-analysis-based, coarse-to-fine table identification algorithm and an X-Y cut table decomposition algorithm are described. We discuss an experimental protocol to evaluate the table detection algorithms. For a total of 1; 125 document pages having 518 table entities and a total of 10; 941 cell entities, our table detection algorithm takes line, word segmentation results as input and obtains around 90% cell correct detection rates.