Chun Ki Cheng’s research while affiliated with Gold Coast University Hospital and other places

This paper presents an enhanced heuristic segmenter (EHS) and an improved neural-based segmentation technique for segmenting cursive words and validating prospective segmentation points respectively. The EHS employs two new features, ligature detection and a neural assistant, to locate prospective segmentation points. The improved neural-based segmentation technique can then be used to examine the prospective segmentation points by fusion of confidence values obtained from left and centre character recognition outputs in addition to the segmentation point validation (SPV) output. The improved neural-based segmentation technique uses a recently proposed feature extraction technique (modified direction feature) for representing the segmentation points and characters to enhance the overall segmentation process. The EHS and the neural-based segmentation technique have been implemented and tested on a benchmark database providing encouraging results.

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. Yes Yes

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