N-Tuple Features for OCR Revisited
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical Analysis and Improved Decision Criteria for the n-Tuple Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
RAM-Based Neural Networks
The DSFPN, a new neural network for optical character recognition
IEEE Transactions on Neural Networks
Fast Convolutional OCR with the Scanning N-Tuple Grid
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Orientational features with the SNT-grid
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A wrapper approach with support vector machines for text categorization
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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In this paper, the scanning n-tuple technique (as introduced by Lucas and Amiri) is studied in pattern recognition tasks, with emphasis placed on methods that improve its recognition performance. We remove potential edge effect problems and optimize the parameters of the scanning n-tuple method with respect to memory requirements, processing speed, and recognition accuracy for a case study task. Next, we report an investigation of self-supervised algorithms designed to improve the performance of the scanning n-tuple method by focusing on the characteristics of the pattern space. The most promising algorithm is studied in detail to determine its performance improvement and the consequential increase in the memory requirements. Experimental results using both small-scale and real-world tasks indicate that this algorithm results in an improvement of the scanning n-tuple classification performance.