MCS for Online Mode Detection: Evaluation on Pen-Enabled Multi-Touch Interfaces
Markus Weber, Marcus Liwicki, Yannik T. H. Schelske, Christopher Schoelzel, Florian Strauß, Andreas Dengel
Proceedings of the 11th International Conference on Document Analysis and Recognition International Conference on Document Analysis and Recognition (ICDAR-11), September 18-21, Beijing, China

Abstract:

This paper proposes a new approach for drawing mode detection in online handwriting. The system classifies groups of ink traces into several categories. The main contributions of this work are as follows. First, we improve and optimize several state-of-the-art recognizers by adding new features and applying feature selections. Second, we use several classifiers for the recognition. Third, we perform multiple classifier combination strategies for combining the outputs. Finally, a large experimental evaluation on two data sets is performed: the publicly available Touch & Write database which has been acquired on a pen-enabled multi-touch surface; and the publicly available IAMonDo-database which serves as a benchmark. In our experiments on the IAM-OnDo-database we achieved a recognition rate of 97 %, which is much higher than other results reported in the literature. On the more balanced multi-touch surface data set we achieved a recognition rate of close to 98 %.

MCS for Online Mode Detection: Evaluation on Pen-Enabled Multi-Touch Interfaces
Markus Weber, Marcus Liwicki, Yannik T. H. Schelske, Christopher Schoelzel, Florian Strauß, Andreas Dengel
Proceedings of the 11th International Conference on Document Analysis and Recognition International Conference on Document Analysis and Recognition (ICDAR-11), September 18-21, Beijing, China

Abstract:

This paper proposes a new approach for drawing mode detection in online handwriting. The system classifies groups of ink traces into several categories. The main contributions of this work are as follows. First, we improve and optimize several state-of-the-art recognizers by adding new features and applying feature selections. Second, we use several classifiers for the recognition. Third, we perform multiple classifier combination strategies for combining the outputs. Finally, a large experimental evaluation on two data sets is performed: the publicly available Touch & Write database which has been acquired on a pen-enabled multi-touch surface; and the publicly available IAMonDo-database which serves as a benchmark. In our experiments on the IAM-OnDo-database we achieved a recognition rate of 97 %, which is much higher than other results reported in the literature. On the more balanced multi-touch surface data set we achieved a recognition rate of close to 98 %.