A background-thinning-based approach for separating and recognizing connected handwritten digit strings

Most algorithms for segmenting connected handwritten digit strings are based on the analysis of the foreground pixel distributions and the features on the upper/lower contours of the image. In this paper, a new approach is presented to segment connected handwritten two-digit strings based on the thinning of background regions. The algorithm first locates several feature points on the background skeleton of a digit image. Possible segmentation paths are then constructed by matching these feature points. With geometric property measures, all the possible segmentation paths are ranked using fuzzy rules generated from a decision-tree approach. Finally, the top ranked segmentation paths are tested one by one by an optimized nearest neighbor classifier until one of these candidates is accepted based on an acceptance criterion. Experimental results on NIST special database 3 show that our approach can achieve a correct classification rate of 92.5% with only 4.7% of digit strings rejected, which compares favorably with the other techniques tested.