Enhancing neural confidence-based segmentation for cursive handwriting recognition
MetadataShow full item record
This paper proposes some directions for enhancing a neural network-based technique for automatically segmenting cursive handwriting. The technique fuses confidence values obtained from left and center character recognition outputs in addition to a Segmentation Point Validation output. Specifically, this paper describes the use of a recently proposed feature extraction technique (Modified Direction Feature) for representing segmentation points and characters to enhance the overall segmentation process. Promising results are presented for Segmentation Point Validation and cursive character recognition on a benchmark dataset. In addition, a new methodology for detecting segmentation paths is presented and evaluated for extracting characters from cursive handwriting.
SEAL 04 and 2004 FIRA Robot world congress
© The Author(s) 2007 Griffith University. The attached file is posted here with permission of the copyright owner for your personal use only. No further distribution permitted.