Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Touching numeral segmentation using water reservoir concept
Pattern Recognition Letters
Top-Down Likelihood Word Image Generation Model for Holistic Word Recognition
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
A Study on Top-down Word Image Generation for Handwritten Word Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A Synthetic Database to Assess Segmentation Algorithms
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A system for processing handwritten bank checks automatically
Image and Vision Computing
Offline handwritten arabic character segmentation with probabilistic model
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Assessing handwitten digit segmentation algorithms
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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We present an automatic recognition system applied to handwritten numeral check amounts. This system is based on a segmentation-by-recognition probabilistic model. The application is described from the field amount localization to the hypothesis generation of amounts. An explicit segmentation algorithm determines cut regions on digit links and provides a multiple spatial representation. The best path for the segmentation is determined by the combination of the recognition scores, segmentation weights and the outputs of a probabilistic parser. Training is done by a bootstrapping technique, which significantly improves the performances of the different algorithms. It also allows the use of a reject class at the recognition step. The system was evaluated on 10000 database images to show its robustness.