Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer

• URL: http://arxiv.org/abs/2107.03704v1
• Date: Thu, 8 Jul 2021 09:25:59 GMT
• Title: Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer
• Authors: Mohamad Wehbi, Tim Hamann, Jens Barth, Bjoern Eskofier
• Abstract summary: This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. 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 time data while writing on regular paper. 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.
• Score: 0.2580765958706854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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