Training with positive and negative data samples: effects on a classifier for hand-drawn geometric shapes

It is quite common in document analysis and symbol recognition to rely on a priori knowledge about the nature of the document in order to locate candidate symbols. It is desirable, but less common, for a segmentation procedure to rely on "a posteriori" feedback from a non-human-guided process to adjust for segmentation errors. For this method to succeed, the feedback must come from a reliable classifier (one that is able to reject negative symbols including miss-segmented symbols). This paper examines the use of positive and negative training data on a nearest-neighbour classifier for hand-drawn geometric shapes. We explore the issues involved in the development of a reliable classifier using this method, and we discuss the trade-off between reliability and correctness.