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 classifier. 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 Levenshtein distance that measures the similarity of two character strings: The distance measure roughly corresponds to the number of character insertions, deletions and replacements that have to be applied to the first string to make it equal to the second string. By applying different costs to these different operations the measure can be tailored to the specific needs of the application. In our system we use a combined single word matching by summing up the Levenshtein distances for each single word of the search string, weighted by the word length.
Applications")?> DACCORD is currently in use for the following purposes:

Application report (in German)")?> report Contact:")?>Alexander Schliep
phone: 0221/470-6039
fax: 0221/470-5160
schliep@zpr.uni-koeln.de