JKanji: Wavelet-based Kanji Recognition
Researchers:
Robert Stockton,
Rahul Sukthankar
Abstract
JKanji is an interactive character completion system that
provides stroke-order-independent recognition of complex
hand-written glyphs such as Japanese kanji or Chinese hanzi.
As the user enters each stroke, JKanji offers a menu of
likely completions, generated from a robust multiscale
matching algorithm augmented with a statistical language
model. Drawbacks of traditional wavelet-based approaches
are addressed by a redundant, phase-shifted basis that is
insensitive to variations of the input character across
quadrant boundaries. Unlike many existing systems, JKanji can
incrementally incorporate new training examples, either to adapt
to the idiosyncrasies of a particular user, or to increase its
vocabulary. On a kanji input task with a vocabulary of 6369
kanji and English characters, JKanji has demonstrated 93%-96%
recognition accuracy and up to 80% reduction in the number of
input strokes. JKanji is computationally efficient, processing
images at 5-10Hz on an inexpensive portable computer, and is
well-suited for integration into personal digital assistants
(PDAs) as an input method. JKanji's recognition system also
processes low-quality digital camera images and has been
integrated into a prototype tourist's guide that interprets
unfamiliar kanji in the environment.
Related Publications
Rahul Sukthankar
(rahuls@cs.cmu.edu),