A two-stage outlier rejection strategy for numerical field extraction in handwritten documents

In this article, we propose a segmentation-driven recognition system which aims at extracting numerical fields from handwritten documents. We show that a crucial point of the system is the rejection ability of the handwritten numeral classifier. Therefore, we propose a simple two-stage outlier rejection strategy, and we show the benefit of this strategy on the numerical field extraction results