Alpha-nets: a recurrent “neural” network architecture with a hidden Markov model interpretation
Speech Communication - Neurospeech
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Decoding Using Markov Source Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Probability Table Compression for Handwritten Character Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Fast Lexicon-Based Word Recognition in Noisy Index Card Images
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Sequence Recognition with Scanning N-Tuple Ensembles
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Orientational features with the SNT-grid
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Hi-index | 0.00 |
This paper introduces a novel high speed convolutional character recognition system. Convolutional mode operation means that no prior localization or segmentation of characters is required, making this mode extremely robust. The method uses a 2-d n-tuple grid to sample the image, but decomposes the address calculations into two onedimensional scans. This simple innovation leads to a very fast system, and speeds in excess of 100,000 recognitions per second have been achieved for a 10-class character recognition problem, when operated in convolutional mode. Quantitative performance results show an error rate of 4.3% on the MNist dataset of isolated hand-written characters. Qualitative results are presented on museum archive card images, indicating that the method has great potential for the character recognition component in a document image analysis system for images of this type.