A Contour Code Feature Based Segmentation For Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A novel approach for structural feature extraction: contour vs. direction
Pattern Recognition Letters
The Neural-based Segmentation of Cursive Words using Enhanced Heuristics
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segment confidence-based binary segmentation (SCBS) for cursive handwritten words
Expert Systems with Applications: An International Journal
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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Abstract: The purpose of this paper is to analyse the performance of our improved segmentation algorithm tested on the CEDAR benchmark database. Segmentation is achieved through the extraction of a wide range of information adjacent to or surrounding suspicious segmentation points. Initially, a heuristic technique is employed to search for structural features and to over-segment each word. For each segmentation point that is located, the left character (preceding the segmentation point), and centre character (centred on the segmentation point) are extracted along with other features from the segmentation area. The aforementioned features are presented to trained character and segmentation point validation neural networks to evaluate a number of confidence values. Finally, the confidence values are fused to obtain the final segmentation decision. Based on a detailed analysis, it was observed that the left and centre character networks increased the accuracy of the segmentation algorithm.