Adaptive Context Processing in On-line Handwritten Character Recognition

We propose a new approach to context processing in on-line handwritten character recognition (OLCR). Based on the observation that writers often repeat the strings that they input, we take the approach of adaptive context processing (ACP). In ACP, the strings input by a writer are automatically added to a dictionary designated for ACP. This dictionary thereby can provide good coverage of the strings a writer inputs. Furthermore, the dictionary is compact enough to be loaded on a small terminal. In our experiments, the first-hit rate of OLCR with ACP was 95.44% after all the strings to be input had been added to the ACP dictionary while that without ACP was 86.09%.