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

arXiv: https://arxiv.org/abs/2107.03704 · HTML: https://mlsanity.com/paper/2107.03704/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [Detecting Handwritten Mathematical Terms with Sensor Based Data](https://mlsanity.com/paper/2109.05594/) (2021)
- [Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition](https://mlsanity.com/paper/2502.20954/) (2025)

## Abstract (arXiv)

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.