A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line, Handwritten Numeral Recognition by Perturbation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving OCR performance using character degradation models and boosting algorithm
Pattern Recognition Letters - special issue on pattern recognition in practice V
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
Handwritten Character Classification Using Nearest Neighbor in Large Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Using tree-grammars for training set expansion in page classification
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
ECML '07 Proceedings of the 18th European conference on Machine Learning
Journal of Artificial Intelligence Research
Writer recognition enhancement by means of synthetically generated handwritten text
Engineering Applications of Artificial Intelligence
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In this paper, a process of expansion of the training set by synthetic generation of handwritten uppercase letters via deformations of natural images is tested in combination with an approximate k-Nearest Neighbor (k-NN) classifier. It has been previously shown [11] [10] that approximate nearest neighbors search in large databases can be successfully used in an OCR task, and that significant performance improvements can be consistently obtained by simply increasing the size of the training set. In this work, extensive experiments adding distorted characters to the training set are performed, and the results are compared to directly adding new natural samples to the set of prototypes.