Now, a Pen that Corrects Your Language

March 15, 2013, By Sanjeev Ramachandran

In this age of touchscreens, even ergonomic keyboards seem old school. But now a project from LernStift seeks to cherish the good old tradition of writing with a technologically advanced pen.

This Munich-based start-up won the accelerator pitch held by WAYRA Germany. The two founders thereby gained the expert support of the international Telefonica network too. Through WAYRA, Telefónica offers Lernstift their international expert network, plus six months free workspace and an important part of their seed investment.

When commercial production begins, the Lernstift product will be available in many countries and in many language versions. It is basically a pen that vibrates when a wrong word is written.


The integrated electronics recognize mistakes as they are being made and give the writer some feedback by vibrating. Lernstift is a great way to learn how to write faster also.

Kids tend to clench when they practice writing. Lernstift’s sensors help them to develop the right feeling for pressure. If the user press the pen too hard, Lernstift answers with a subtle vibration which intensifies with increased pressure.

Everything you write or draw with Lernstift will be available for digital use, too. For that a network module will be attached to the pen. This will aid in connecting to the user’s Wi-Fi and send data to your other devices.


The digital pens that are currently available require paper or a solid writing pad, or use external movement sensors to register the pen’s movements. But Lernstift works differently. Its special motion sensor technology is being patented. The pen recognizes words, gestures, and symbols even if the user draws them in the air. This opens up new possibilities for whiteboard applications.

Lernstift will soon launch a crowd-funding project via Kickstarter. The aim is to finance parts of the ongoing product development through the pledges of Kickstarter backers. A few days ago, the Lernstift team had conducted some preliminary hardware tests using a “primitive” prototype. The objective was to measure and analyze the performance of different motion sensors that deliver the raw data for the handwriting recognition.

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