High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.
A novel feature extraction technique for the recognition of segmented handwritten characters
2003-01-01
231814 byte
Conference paper
Electronic Resource
English
A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters
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