A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Contour Code Feature Based Segmentation For Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Handwriting Segmentation Contest
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Features extraction for soccer video semantic analysis: current achievements and remaining issues
Artificial Intelligence Review
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This paper analyzes the improved performance of our proposed character segmentation algorithm in comparison to others presented in the literature from accuracy and computational complexity points of view. The training set is taken from IAM and test set is from CEDAR benchmark databases. Segmentation is achieved by analyzing character@?s geometric features and ligatures which are strong points for segmentation in cursive handwritten words. Following pre-processing, a new heuristic technique is employed to over-segment each word at potential segmentation points. Subsequently, a simple criterion is adopted to come out with fine segmentation points based on character shape analysis. Finally, the fine segmentation points are fed to train neural network for validating segment points to enhance segmentation accuracy. Based on detailed analysis and comparison, it is observed that proposed approach enhances the cursive script segmentation accuracy with minimum computational complexity.