This thesis describes a method to find a part of online data in an offline document. This method is able to find the offline document that belongs to the online data from a set of offline documents, or vice versa. In order to optimize the mapping between the online and the offline data, an optimal rotation and resizing of the online data is calculated. This is useful since it produces a better mapping between online and offline data, which makes several methods that are only applicable for online data available for offline data, and vice versa. Results show that this method can be used for finding the offline document that belongs to certain online data, since it succeeded in 98.07% of the cases for the used dataset. The results also show that computing the optimal rotation and resize factor significantly improves the mapping between online and offline data. This improvement is 6.56% for the used dataset.

1. Mapping online data on offline documents Artificial handwriting recognition Stefan Kennedie

4. Properties of online and offline data Known Unknown Width of ink Unknown Known Location of pen known when not on paper Unknown Known Velocity and acceleration Unknown Known Order of writing Offline Online