Figure 1 shows the changes in recognition error rates over time. Apparently, users need to change their handwriting style somewhat to accommodate current recognition software and write in ways that the machine finds easier to understand. This result shows that even handwriting input cannot be considered as requiring zero learning time as an interaction technique. To glnlrata intelligent indexing that allows context-sensitive information retrieval, a system must be abld to acavire knowledge directly through interaction with users. It utilizes on-line user information requirements and sussestions either to reinforce current indexing in case of success or to generate new knowledge in case of failure.
Question marks indicate characters for which the recognizer declined to make a guess.
Figure 2 shows a sample abstract as it was recognized by the pen machine. In many other abstracts, however, important terms were repeated several times with at least one occurrence recognized correctly. We use both LSI and keyword methods to match each reviewer's interest profile with all the abstracts. Retrieval quality can thus be measured as the mean rated relevance of abstracts that best matched each reviewer's interests. Retrieval of 47 handwritten conference paper abstracts is certainly not a typical case of the use of a personal digital assistant.