Knowledge-based English cursive script segmentation
Pattern Recognition Letters
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Segmentation Performance on the CEDAR Benchmark Database
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Moment normalization of handprinted characters
IBM Journal of Research and Development
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Binary segmentation algorithm for English cursive handwriting recognition
Pattern Recognition
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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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.