Multi-Language Online Handwriting Recognition
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
Publication Year
2016
Authors
Daniel Keysers, Thomas Deselaers, Henry A. Rowley, Li-Lun Wang, Victor Carbune
BibTeX
Abstract
We describe Google's online handwriting recognition system that currently supports
22 scripts and 97 languages. The system's focus is on fast, high-accuracy text
entry for mobile, touch-enabled devices. We use a combination of state-of-the-art
components and combine them with novel additions in a flexible framework. This
architecture allows us to easily transfer improvements between languages and
scripts. This made it possible to build recognizers for languages that, to the best
of our knowledge, are not handled by any other online handwriting recognition
system. The approach also enabled us to use the same architecture both on very
powerful machines for recognition in the cloud as well as on mobile devices with
more limited computational power by changing some of the settings of the system. In
this paper we give a general overview of the system architecture and the novel
components, such as unified time- and position-based input interpretation,
trainable segmentation, minimum-error rate training for feature combination, and a
cascade of pruning strategies. We present experimental results for different
setups. The system is currently publicly available in several Google products, for
example in Google Translate and as an input method for Android devices.
