Message Extraction from Printed Documents - A Complete Solution
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A method for table structure analysis using DP matching
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Model-based analysis of printed tables
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Robust table-form structure analysis based on box-driven reasoning
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Making Documents Work: Challenges for Document Understanding
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Model based table cell detection and content extraction from degraded document images
Proceeding of the workshop on Document Analysis and Recognition
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The ScanTab system represents a knowledge-based approach to table recognition in scanned documents. In contrast to most systems which recognize tables by grouping layout information, our system uses predefined information about which table types may appear in the documents. This enables a very accurate detection able to cope with distorted tables and tables providing little layout information, e.g., no lines, bad alignment, or few rows. Table recognition starts with the detection of the table header. Afterwards, this header is compared with table headers of known reference tables. Having determined the correct reference table, the information kept in the knowledge base is utilized to compute the complete table structure. A graphical user interface allows an easy and fast specification of reference tables.