Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer
This work addresses the challenge of digitizing handwriting in real-world applications, offering a practical solution for writing on normal paper, though it is incremental as it builds on existing sensor-based methods.
The paper tackles the problem of online handwriting recognition on plain paper using a sensor-equipped pen, achieving promising results for writer-independent character recognition without user-specific training.
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.