A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters

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.

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Michael Blumenstein, Brijesh Verma, H. Basli
• Keywords: Character Recognition, Document Analysis, Handwritten Word Recognition, ICDAR 2003, Word Recognition System