The OnHW Dataset

Handwriting is an important skill. In children, it benefits motor control, visual-motor integration, proprioception and sustained attention. Studies have also shown that handwriting improves the brain’s processing capabilities compared to typing notes on a laptop. Furthermore, it enhances the users’ ideation abilities. Despite these scientific advantages, the main reason for most people to use pen and paper is just the comfortable, analog, natural look-and-feel.


The OnHW-chars Dataset

The dataset can be downloaded here (895 MB). (Update:2021-06-30)

For more information, see the readme.pdf file.

Data Acquisition

To obtain the sensor data, we implemented a recording app that connects to a DigiPen and tells the volunteers which letter to write. These are some of the constraints that were met during the recordings:

  • The recordings were conducted sitting on a chair in front of a table.
  • The writing surface was horizontal.
  • Normal, white paper sheets (about 80g/m^2) were used to write upon.
  • The sheet was padded by five additional sheets.
  • There was no guideline concerning the size of the handwriting. The subjects were asked to use a size that is natural for them.
  • There was no guideline concerning the way of holding the pen. The subjects were asked to use a position that is natural for them.
  • The volunteers were asked to make sure the STABILO logo faces up to avert different pen orientations.
  • Participants could choose freely between print and cursive writing styles.
  • Only right-handed recordings are released.



The STABILO Digipen is a sensor-enhanced ballpoint pen with internal data processing capabilities. Its Bluetooth module enables live streaming sensor data to a connected device. The pen’s internal power source lasts at least 17 hours and is recharcheable via micro USB. Its diameter is 15mm, its overall length is 167mm and it weighs 25g which, along with its ergonomic soft-touch grip zone, makes it comfortable and easy-to-use.

Each Digipen is equipped with five sensors.

  • Front accelerometer (STM LSM6DSL)
  • Gyroscope (STM LSM6DSL)
  • Rear accelerometer (Freescale MMA8451Q)
  • Magnetometer (ALPS HSCDTD008A)
  • Force sensor (ALPS HSFPAR003A)

Sensor Data

The sensors’ raw data stream is provided in the files called sensor_data.csv. Each file consists of 15 columns:

  • Millis: The timestamp when the data were processed on the tablet computer that the pen was connected to during recording
  • Acc1 X, Acc1 Y, Acc1 Z: The values of the front accelerometer in three dimensions
  • Acc2 X, Acc2 Y, Acc2 Z: The values of the rear accelerometer in three dimensions
  • Gyro X, Gyro Y, Gyro Z: The gyroscope values in three dimensions
  • Mag X, Mag Y, Mag Z: The magnetometer values in three dimensions
  • Force: The force with which the pen tip touches the surface
  • Time: A sample counter



If you use the OnHW dataset, please cite:

Felix Ott*, Mohamad Wehbi*, Tim Hamann, Jens Barth, Björn Eskofier, and Christopher Mutschler. „The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning.“ In Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT), vol. 4, no. 3, article 92, Cancún, Mexico, Sept. 2020



  author = {Felix Ott and Mohamad Wehbi and Tim Hamann and Jens Barth and Bj{\"o}rn Eskofier and Christopher Mutschler},
  title = {{The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning}},
  booktitle = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT)},
  volume = {4(3)},
  article = {92},
  address = {Canc\'{u}n, Mexico},
  month = sep,
  year = {2020}