Comparison of Adaptive Strategies for On-Line Character Recognition

Results on a comparison of adaptive recognition techniques for on-line recognition of handwritten Latin alphabets are presented. The classification strategies compared are based on first compressing or distilling a large database of handwritten characters to a small set of character prototypes. Each adaptive classifier then either modifies the original prototypes or conditionally adds new prototypes when they become available from the user of the system. In each case, the classification decision uses the 1-Nearest Neighbor (1-NN) rule for the distances between the input character and the stored prototypes. The distances are calculated using Dynamic Time Warping (DTW). One of the adaptive learning strategies features an extension of the neural Learning Vector Quantization (LVQ) algorithm to the DTW distance metric. All the methods concerned exhibit automatic unsupervised learning from user input simultaneously with the normal mode of operation. The presented experiments show that the assessed methods produce different tradeoffs between the accuracy and complexity of classification. Every version is, however, able to adapt to the user’s writing style with only a very few — say some tens of — handwritten characters.