Layout Recognition of Multi-Kinds of Table-Form Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of characters from form documents by feature point clustering
Pattern Recognition Letters
Finding straight lines in drawings
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
The T-Recs Table Recognition and Analysis System
DAS '98 Selected Papers from the Third IAPR Workshop on Document Analysis Systems: Theory and Practice
Document Layout Structure Extraction Using Bounding Boxes of Different Entities
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Efficient techniques for telephone company line drawing interpretation
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Form Identification Based on Cell Structure
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
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
A Recursive Analysis for Form Cell Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Field Extraction Method from Existing Forms Transmitted by Facsimile
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A table-form extraction with artefact removal
Proceedings of the 2007 ACM symposium on Applied computing
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An artefact identification method for handwritten filled table-forms is presented. Artefacts in table-forms are smudges and overlaps between handwritten data and line segments which increase the complexity of table-form interpretation. After reviewing some knowledge-based methods, a novel artefact identification method to improve table-form interpretation is presented. The proposed method aims to detect, identify and remove table-form artefacts with little use of previous knowledge. Experiments show the significance of using the proposed artefact identification method to improve table-form interpretation rates.