Despite the current dominance of typed text, writing by hand remains the most natural mean of written communication and information keeping. Still, digital pen input provides limited user experience and lacks flexibility, as most of the manipulations are performed on a digitalized version of the text. In this paper, we present our prototype that enables spellchecking for handwritten text: it allows users to interactively correct misspellings directly in a handwritten script. We plan to study the usability of the proposed user interface and its acceptance by users. Also, we aim to investigate how user feedback can be used to incrementally improve the underlying recognition models.

Many features have been proposed for encoding the input signal from digital pens and touch-based interaction. They are widely used for analyzing and classifying handwritten texts, sketches, or gestures. Although they are well defined mathematically, many features are non-trivial and therefore difficult to understand for a human. In this paper, we present an application that visualizes a subset from 114 digital pen features in real-time while drawing. It provides an easy-to-use interface that allows application developers and machine learning practitioners to learn how digital pen features encode their inputs, helps in the feature selection process, and enables rapid prototyping of sketch and gesture classifiers.