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What Am I Writing: Classification of On-Line Handwritten Sequences

Junaid Younas; Stefan Fritsch; Gerald Pirkl; Sheraz Ahmed; Muhammad Imran Malik; Faisal Shafait; Paul Lukowicz
In: Ioannis Chatzigiannakis; Yoshito Tobe; Paulo Novais; Oliver Amft (Hrsg.). Intelligent Environments 2018 - Workshop Proceedings of the 14th International Conference on Intelligent Environments. International Conference on Intelligent Environments (IE-2018), Pages 417-426, Ambient Intelligence and Smart Environments, Vol. 23, ISBN 978-1-61499-873-0 (print) | 978-1-61499-874-7 (online), IOS Press, 2018.


This paper presents a novel approach for classification of online handwritten sequences into text, equations, and plots. This classification helps in identifying the progress of student/learner while attempting different problems in context of classroom equipped with tablets, iPads. Furthermore, it serves as a feedback (for both students and instructors) to analyse the writing behaviour and understanding capabilities of the student. The presented approach is based on an ensemble of different machine learning classifiers, where not only the individual sequences are classified but also the contextual information is used to refine the classification results. To train and test the system, a real-world dataset consisting up of 11,601 sequences was collected from 20 participants. Evaluation results on the real dataset shows that the presented system, when tested in person independent settings, is capable of classifying handwritten on-line sequences with an overall accuracy of 92%.

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