Associative data bases")?>
The classification of handwritten characters often leads to ambiguities,
i.e. a certain character can have different possible interpretations,
e.g. "1" and "7" often look alike and cannot be distinguished by the
Using context information these ambiguities can usually be solved.
If, in the case above, the digit is part of an account number and
only one of the two possible digit strings results in an existing
account number a decision can be made. If both possible account
numbers exist, the name of the account owners can be compared
and the best match decides.
Based on our recognizer for handwritten characters we thus began to develop the associative data base DACCORD in summer 1995. The goal of this project was an efficient portable application that shows high performance rates using large data bases even on standard PCs. The developed system could soon be installed in one of Germany's leading banks where up to 10 credit transfer forms per second can be disambiguated using a data base of approximately 2.5 million bank accounts. The system runs on a PC with Microsoft Windows NT.
Ambiguous recognizer output from bad handwriting causes a significantly enlarged search space. In our system data bases of a size of up to 2 GByte can now be searched efficiently using fast hash algorithms. The comparison of the recognizer output and the data base entries is based on the
Applications")?> DACCORD is currently in use for the following purposes: