An automatic reading system for handwritten numeral amounts on French checks

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