Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Touching numeral segmentation using water reservoir concept
Pattern Recognition Letters
An approach for locating segmentation points of handwritten digit strings using a neural network
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
An Evolutionary Algorithm for General Symbol Segmentation
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A System for Cursive Handwritten Address Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
HMM-KNN Word Recognition Engine for Bank Cheque Processing
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new method of reading the handwritten zip codes in the U.S. Postal Services CD-ROM database is presented. Zip code images are binarized, segmented and recognised. A recognition driven method for splitting multiple connected digits has been developed; for grouping together of broken digits, the system targets components with near-touching stroke tips, 5-hats, and 4-Ls. The digit recogniser is a majority vote combination of 3 neural networks with a zero rejection performance of 96.53% on the 2711 imperfectly segmented digits in the cedarbs test set. With digit splitting capability disabled, the system performance on the 930 whole zip codes of the test set is 61.0% correct with no errors when up to two rejected symbol positions are allowed. With digit splitting enabled the performance rises to 66.3%.