Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Unsupervised language acquisition
Unsupervised language acquisition
Linguistic structure as composition and perturbation
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Text-Independent Writer Identification and Verification on Offline Arabic Handwriting
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Offline Writer Identification Using K-Adjacent Segments
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
| Hi-index | 0.10 |
Many present recognition systems take advantage of ground-truthed datasets for training, evaluating and testing. But the creation of ground-truthed datasets is a tedious task. This paper proposes an iterative unsupervised handwritten graphical symbols learning framework which can be used for assisting such a labeling task. Initializing each stroke as a segment, we construct a relational graph between the segments where the nodes are the segments and the edges are the spatial relations between them. To extract the relevant patterns, a quantization of segments and spatial relations is implemented. Discovering graphical symbols becomes then the problem of finding the sub-graphs according to the Minimum Description Length (MDL) principle. The discovered graphical symbols will become the new segments for the next iteration. In each iteration, the quantization of segments yields the codebook in which the user can label graphical symbols. This original method has been first applied on a dataset of simple mathematical expressions. The results reported in this work show that only 58.2% of the strokes have to be manually labeled.