Wehbi M, Hamann T, Barth J, Eskofier B (2020)
Publication Type: Conference contribution
Publication year: 2020
ISBN: 9781728199665
DOI: 10.1109/ICFHR2020.2020.00061
Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a realtime handwriting recognition system to be used for writing on normal paper.
APA:
Wehbi, M., Hamann, T., Barth, J., & Eskofier, B. (2020). Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer. In IEEE (Eds.), Proceedings of the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR). Dortmund, DE.
MLA:
Wehbi, Mohamad, et al. "Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer." Proceedings of the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), Dortmund Ed. IEEE, 2020.
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